NeurIPS 2020 Accepted Papers 1899

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Continuous Surface Embeddings
Natalia Neverova (Facebook AI Research) · David Novotny (Facebook AI Research) · Marc Szafraniec (Facebook AI Research) · Vasil Khalidov (Facebook AI Research) · Patrick Labatut (Facebook AI Research) · Andrea Vedaldi (University of Oxford / Facebook AI Research)

Improving model calibration with accuracy versus uncertainty optimization
Ranganath Krishnan (Intel Labs) · Omesh Tickoo (Intel)

Few-shot Image Generation with Elastic Weight Consolidation
Yijun Li (Adobe Research) · Richard Zhang (Adobe) · Jingwan (Cynthia) Lu (Adobe Research) · Eli Shechtman (Adobe Research, US)

Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks
Umut Simsekli (Institut Polytechnique de Paris/ University of Oxford) · Ozan Sener (Intel Labs) · George Deligiannidis (Oxford) · Murat Erdogdu (University of Toronto)

Quantitative Propagation of Chaos for SGD in Wide Neural Networks
Valentin De Bortoli (ENS Paris-Saclay) · Alain Durmus (ENS Paris Saclay) · Xavier Fontaine (ENS Paris-Saclay) · Umut Simsekli (Institut Polytechnique de Paris/ University of Oxford)

Stochastic Optimization for Performative Prediction
Celestine Mendler-Dünner (UC Berkeley) · Juan Perdomo (University of California, Berkeley) · Tijana Zrnic (UC Berkeley) · Moritz Hardt (University of California, Berkeley)

Explicit Regularisation in Gaussian Noise Injections
Alexander Camuto (University of Oxford & The Alan Turing Institute) · Matthew Willetts (University of Oxford) · Umut Simsekli (Institut Polytechnique de Paris/ University of Oxford) · Stephen J Roberts (University of Oxford) · Chris C Holmes (University of Oxford)

Dual Instrumental Variable Regression
Krikamol Muandet (Max Planck Institute for Intelligent Systems) · Arash Mehrjou (Max Planck Institute) · Si Kai Lee (Chicago Booth School of Business) · Anant Raj (Max Planck Institute for Intelligent Systems)

Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness
Long Zhao (Rutgers University) · Ting Liu (Google) · Xi Peng (University of Delaware) · Dimitris Metaxas (Rutgers University)

Strongly Incremental Constituency Parsing with Graph Neural Networks
Kaiyu Yang (Princeton University) · Jia Deng (Princeton University)

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Juntang Zhuang (Yale University) · Tommy Tang (University of Illinois Urbana-Champaign) · Sekhar C Tatikonda (Yale University) · Nicha Dvornek (Yale University) · Yifan Ding (University of Central Florida) · Xenophon Papademetris (Yale University) · James Duncan (Yale University)

A Simple and Practical Algorithm for Private Multivariate Mean and Covariance Estimation
Sourav Biswas (University of Waterloo) · Yihe Dong (Microsoft) · Gautam Kamath (University of Waterloo) · Jonathan Ullman (Northeastern University)

The Discrete Gaussian for Differential Privacy
Clément L Canonne (IBM Research) · Gautam Kamath (University of Waterloo) · Thomas Steinke (IBM Almaden)

Private Identity Testing for High-Dimensional Distributions
Clément L Canonne (IBM Research) · Gautam Kamath (University of Waterloo) · Audra McMillan (Northeastern/Boston University) · Jonathan Ullman (Northeastern University) · Lydia Zakynthinou (Northeastern University)

Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding
Rishi Sonthalia (University of Michigan) · Anna Gilbert (University of Michigan)

Learning Kernel Tests Without Data Splitting
Jonas Kübler (MPI for Intelligent Systems, Tübingen) · Wittawat Jitkrittum (Max Planck Institute for Intelligent Systems) · Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen) · Krikamol Muandet (Max Planck Institute for Intelligent Systems)

Generative View Synthesis: From Single-view Semantics to Novel-view Images
Tewodros Amberbir Habtegebrial (Technische Universität Kaiserslautern) · Varun Jampani (Google) · Orazio Gallo (NVIDIA Research) · Didier Stricker (DFKI)

Rational neural networks
Nicolas Boulle (University of Oxford) · Yuji Nakatsukasa (University of Oxford) · Alex J Townsend (Cornell University)

Adversarial Attack on Graph Neural Networks with Limited Node Access
Jiaqi Ma (University of Michigan) · Shuangrui Ding (University of Michigan) · Qiaozhu Mei (University of Michigan)

Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
Iro Laina (University of Oxford) · Ruth Fong (University of Oxford) · Andrea Vedaldi (Facebook AI Research and University of Oxford)

Space-Time Correspondence as a Contrastive Random Walk
Allan Jabri (UC Berkeley) · Andrew Owens (UC Berkeley) · Alexei Efros (UC Berkeley)

Relabeling Experience with Inverse RL: Hindsight Inference for Policy Improvement
Ben Eysenbach (Carnegie Mellon University) · XINYANG GENG (UC Berkeley) · Sergey Levine (UC Berkeley) · Russ Salakhutdinov (Carnegie Mellon University)

Self-supervised Co-Training for Video Representation Learning
Tengda Han (University of Oxford) · Weidi Xie (University of Oxford) · Andrew Zisserman (DeepMind & University of Oxford)

Rel3D: A Minimally Contrastive Benchmark or Grounding Spatial Relations in 3D
Ankit Goyal (Princeton University) · Kaiyu Yang (Princeton University) · Dawei Yang (University of Michigan) · Jia Deng (Princeton University)

Is normalization indispensable for training deep neural network?
Jie Shao (Fudan University) · Kai Hu (Carnegie Mellon University) · Changhu Wang (ByteDance.Inc) · Xiangyang Xue (Fudan University) · Bhiksha Raj (Carnegie Mellon University)

Efficient Exact Verification of Binarized Neural Networks
Kai Jia (MIT) · Martin Rinard (MIT)

ConvBERT: Improving BERT with Span-based Dynamic Convolution
Zi-Hang Jiang (National University of Singapore) · Weihao Yu (National University of Singapore) · Daquan Zhou (National University of Singapore) · Yunpeng Chen (Yitu Technology) · Jiashi Feng (National University of Singapore) · Shuicheng Yan (National University of Singapore)

On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law
Damien Teney (University of Adelaide) · Ehsan Abbasnejad (University of Adelaide) · Kushal Kafle (Adobe Research) · Robik Shrestha (Rochester Institute of Technology) · Christopher Kanan (PAIGE.AI / RIT / CornellTech) · Anton van den Hengel (University of Adelaide)

Labelling unlabelled videos from scratch with multi-modal self-supervision
Yuki Asano (University of Oxford) · Mandela Patrick (University of Oxford) · Christian Rupprecht (University of Oxford) · Andrea Vedaldi (University of Oxford / Facebook AI Research)

Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
Jingtong Su (Peking University) · Yihang Chen (Peking University) · Tianle Cai (Peking University) · Tianhao Wu (Peking University) · Ruiqi Gao (Peking University) · Liwei Wang (Peking University) · Jason Lee (Princeton University)

On the equivalence of molecular graph convolution and molecular wave function with poor basis set
Masashi Tsubaki (National Institute of Advanced Industrial Science and Technology (AIST)) · Teruyasu Mizoguchi (University of Tokyo)

Generative 3D Part Assembly via Dynamic Graph Learning
佳磊 黄 (Peking University) · Guanqi Zhan (Peking University) · Qingnan Fan (Stanford University) · Kaichun Mo (Stanford University) · Lin Shao (Stanford University) · Baoquan Chen (Shandong University) · Leonidas J Guibas (stanford.edu) · Hao Dong (Peking University)

Prophet Attention: Predicting Attention with Future Attention for Improved Image Captioning
Fenglin Liu (Peking University) · Xuancheng Ren (Peking University) · Xian Wu (Tencent Medical AI Lab) · Shen Ge (Tencent Medical AI Lab) · Wei Fan (Tencent) · Yuexian Zou (Peking University) · Xu Sun (Peking University)

Heuristic Adversarial Domain Adaptation
shuhao cui (ict cas) · Xuan Jin (Alibaba Turing Lab, Alibaba Group) · Shuhui Wang (VIPL,ICT,Chinese academic of science) · Yuan He (Alibaba Group) · Qingming Huang (University of Chinese Academy of Sciences)

A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings
Junhyung Park (MPI for Intelligent Systems, Tübingen) · Krikamol Muandet (Max Planck Institute for Intelligent Systems)

PLANS: Robust Program Learning from Neurally Inferred Specifications
Raphaël Dang-Nhu (ETH Zürich)

AOT: Appearance Optimal Transport Model for Face Swapping
Hao Zhu (Anhui University) · Chaoyou Fu (Institute of Automation, Chinese Academy of Sciences) · Qianyi Wu (Sensetime) · Wayne Wu (Tsinghua University) · Chen Qian (SenseTime) · Ran He (NLPR, CASIA)

Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows
Cheng Zhang (Peking University)

The Adaptive Complexity of Maximizing a Gross Substitutes Valuation
Ron Kupfer (The Hebrew University of Jerusalem) · Sharon Qian (Harvard) · Eric Balkanski (Harvard University) · Yaron Singer (Harvard University)

Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher
Guangda Ji (Peking University) · Zhanxing Zhu (Peking University)

Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
Zhisheng Xiao (The University of Chicago) · Qing Yan (University of Chicago) · Yali Amit (University of Chicago)

Bayesian Probabilistic Numerical Integration with Tree-Based Models
Harrison Zhu (Imperial College London) · Xing Liu (Imperial College London) · Ruya Kang (Brown University) · Zhichao Shen (University of Oxford) · Seth Flaxman (Imperial College London) · Francois-Xavier Briol (University of Cambridge)

Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization
Qi Chen (Johns Hopkins University) · Lin Sun (Samsung, Stanford, HKUST) · Ernest Cheung (Samsung) · Alan Yuille (Johns Hopkins University)

Provably Efficient Exploration for RL with Unsupervised Learning
Fei Feng (University of California, Los Angeles) · Ruosong Wang (Carnegie Mellon University) · Wotao Yin (Alibaba US, DAMO Academy) · Simon Du (Institute for Advanced Study) · Lin Yang (UCLA)

Probabilistic Time Series Forecasting with Shape and Temporal Diversity
Vincent LE GUEN (CNAM, Paris, France) · Nicolas THOME (Cnam (Conservatoire national des arts et métiers))

Decision-Making with Auto-Encoding Variational Bayes
Romain Lopez (UC Berkeley) · Pierre Boyeau (UC Berkeley) · Nir Yosef (UC Berkeley) · Michael Jordan (UC Berkeley) · Jeffrey Regier (University of Michigan)

RetroXpert: Decompose Retrosynthesis Prediction like A Chemist
Chaochao Yan (The University of Texas at Arlington) · Qianggang Ding (Tsinghua University) · Peilin Zhao (Tencent AI Lab) · Shuangjia Zheng (SUN YAT-SEN UNIVERSITY) · JINYU YANG (The University of Texas at Arlington) · Yang Yu (Tencent AI Lab) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
Huanrui Yang (Duke University) · Jingyang Zhang (Duke University) · Hongliang Dong (Duke University) · Nathan Inkawhich (Duke University) · Andrew Gardner (Radiance Technologies) · Andrew Touchet (Radiance Technologies) · Wesley Wilkes (Radiance Technologies) · Heath Berry (Radiance Technologies) · Hai Li (Duke University)

CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
Davis Rempe (Stanford University) · Tolga Birdal (Technical University of Munich) · Yongheng Zhao (University of Padova) · Zan Gojcic (ETH Zürich) · Srinath Sridhar (Stanford University) · Leonidas J Guibas (stanford.edu)

Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation
Ping Hu (Boston University) · Stan Sclaroff (Boston University) · Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)

Task-Oriented Feature Distillation
Linfeng Zhang (Tsinghua University) · Yukang Shi (Xi’an Jiaotong University) · Zuoqiang Shi (Tsinghua University) · Kaisheng Ma (Tsinghua University) · Chenglong Bao (Tsinghua university)

Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
Zifeng Wang (Tsinghua-Berkeley Shenzhen Institute, Tsinghua University) · Xi Chen (Tencent) · Rui Wen (Tencent) · Shao-Lun Huang (Tsinghua-Berkeley Shenzhen Institute) · Ercan E Kuruoglu (Tsinghua-Berkeley Shenzhen Institute) · Yefeng Zheng (Tencent)

Sample Complexity of Uniform Convergence for Multicalibration
Eliran Shabat (Tel-Aviv University) · Lee Cohen (Tel Aviv University) · Yishay Mansour (Tel Aviv University / Google)

What if Neural Networks had SVDs?
Alexander Mathiasen (Aarhus University) · Frederik Hvilshøj (Aarhus University) · Jakob Rødsgaard Jørgensen (Aarhus University) · Anshul Nasery (Indian Institute of Technology) · Davide Mottin (Aarhus University)

Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
Yixiao Ge (The Chinese University of Hong Kong) · Feng Zhu (SenseTime Research) · Dapeng Chen (The Chinese University of Hong Kong) · Rui Zhao (The Chinese University of Hong Kong) · hongsheng Li (cuhk)

Learning from Positive and Unlabeled Data with Arbitrary Positive Shift
Zayd Hammoudeh (University of Oregon) · Daniel Lowd (University of Oregon)

Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
Hu Liu (JD.com) · Jing LU (Business Growth BU JD.com) · Xiwei Zhao (JD.com) · Sulong Xu (JD.com) · Hao Peng (JD.com) · Yutong Liu (JD.com) · Zehua Zhang (JD.com) · Jian Li (JD.com) · Junsheng Jin (JD.com) · Yongjun Bao (JD.com) · Weipeng Yan (JD.com)

Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts
Maksim Riabinin (Yandex, Higher School of Economics) · Anton Gusev (none)

Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
Micah Goldblum (University of Maryland) · Liam Fowl (University of Maryland) · Tom Goldstein (University of Maryland)

Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency
Fang Zhao (Inception Institute of Artificial Intelligence) · Shengcai Liao (Inception Institute of Artificial Intelligence) · Kaihao Zhang (Australian National University) · Ling Shao (Inception Institute of Artificial Intelligence)

One-bit Supervision for Image Classification
hengtong hu (Hefei University of Technology) · Lingxi Xie (Huawei Noah's Ark Lab) · Zewei Du (Huawei Noah's Ark Lab) · Richang Hong (Hefei University of Technology) · Qi Tian (Huawei Noah’s Ark Lab)

Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning
Pan Zhou (Salesforce) · Jiashi Feng (National University of Singapore) · Chao Ma (Princeton University) · Caiming Xiong (Salesforce) · Steven Chu Hong Hoi (Salesforce) · Weinan E (Princeton University)

RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Oindrila Saha (Microsoft Research) · Aditya Kusupati (University of Washington) · Harsha Vardhan Simhadri (Microsoft Research) · Manik Varma (Microsoft Research India) · Prateek Jain (Microsoft Research)

Self-Supervised Relationship Probing
Jiuxiang Gu (Adobe Research) · Jason Kuen (Adobe Research) · Shafiq Joty (Nanyang Technological University) · Jianfei Cai (Monash University) · Vlad Morariu (Adobe Research) · Handong Zhao (Adobe Research) · Tong Sun (Adobe Research)

Scientific Control for Reliable Neural Network Pruning
Yehui Tang (Peking University) · Yunhe Wang (Huawei Noah's Ark Lab) · Yixing Xu (Huawei Noah's Ark Lab) · Dacheng Tao (University of Sydney) · Chunjing XU (Huawei Technologies) · Chao Xu (Peking University) · Chang Xu (University of Sydney)

Group Contextual Encoding for 3D Point Clouds
Xu Liu (The University of Tokyo) · Chengtao Li (MIT) · Jian Wang (Carnegie Mellon University) · Jingbo Wang (Peking University) · Boxin Shi (Peking University) · Xiaodong He (JD AI research)

Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
Yawei Luo (Zhejiang University) · Ping Liu (UTS) · Tao Guan (Huazhong University of Science and Technology) · Junqing Yu (Huazhong University of Science & Technology) · Yi Yang (UTS)

Pruning Filter in Filter
Fanxu Meng (Harbin Institute of Technology, Shenzhen) · Hao Cheng (Tencent) · Ke Li (Tencent) · Huixiang Luo (Tencent) · Xiaowei Guo (Tencent Youtu Lab) · Guangming Lu (Harbin Institute of Technology, Shenzhen) · Xing Sun (Tencent)

Learning to Orient Surfaces by Self-supervised Spherical CNNs
Riccardo Spezialetti (University of Bologna) · Federico Stella (Università di Bologna) · Marlon Marcon (Federal University of Technology - Paraná) · Luciano Silva (UFPR) · Samuele Salti (University of Bologna) · Luigi Di Stefano (University of Bologna)

Beta R-CNN: Looking into Pedestrian Detection from Another Perspective
Zixuan Xu (Peking University) · Banghuai Li (Megvii) · Ye Yuan (Megvii) · Anhong Dang (Peking University)

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization
Sangwon Jung (SKKU) · Hongjoon Ahn (Sunkyunkwan University) · Sungmin Cha (Sungkyunkwan University) · Taesup Moon (Sungkyunkwan University (SKKU))

HOI Analysis: Integrating and Decomposing Human-Object Interaction
Yong-Lu Li (Shanghai Jiao Tong University) · Xinpeng Liu (Shanghai Jiao Tong University) · Xiaoqian Wu (Shanghai Jiao Tong University) · Yizhuo Li (Shanghai Jiao Tong University) · Cewu Lu (Shanghai Jiao Tong University)

Generalised Bayesian Filtering via Sequential Monte Carlo
Ayman Boustati (University of Warwick) · Omer Deniz Akyildiz (University of Warwick) · Theodoros Damoulas (University of Warwick & The Alan Turing Institute) · Adam Johansen (University of Warwick)

A Ranking-based, Balanced Loss Function for Both Classification and Localisation in Object Detection
Kemal Oksuz (Middle East Technical University) · Baris Can Cam (Middle East Technical University) · Emre Akbas (Middle East Technical University) · Sinan Kalkan (Middle East Technical University)

StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks
Guangmo Tong (University of Delaware)

PAC-Bayes Analysis Beyond the Usual Bounds
Omar Rivasplata (DeepMind & UCL) · Ilja Kuzborskij (DeepMind) · Csaba Szepesvari (DeepMind / University of Alberta) · John Shawe-Taylor (UCL)

Fast and Flexible Temporal Point Processes with Triangular Maps
Oleksandr Shchur (Technical University of Munich) · Nicholas Gao (Technical University of Munich) · Marin Biloš (Technical University of Munich) · Stephan Günnemann (Technical University of Munich)

Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis
Ye Yuan (Carnegie Mellon University) · Kris Kitani (Carnegie Mellon University)

Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes
Zaiwei Chen (Georgia Institute of Technology) · Siva Theja Maguluri (Georgia Institute of Technology) · Sanjay Shakkottai (University of Texas at Austin) · Karthikeyan Shanmugam (IBM Research, NY)

High-Dimensional Learning with CART
Jason Klusowski (Princeton University)

Learning About Objects by Learning to Interact with Them
Martin Lohmann (Allen Institute for Artificial Intelligence) · Jordi Salvador (Allen Institute for AI) · Aniruddha Kembhavi (Allen Institute for Artificial Intelligence (AI2)) · Roozbeh Mottaghi (Allen Institute for Artificial Intelligence)

Softmax Deep Double Deterministic Policy Gradients
Ling Pan (Tsinghua University) · Qingpeng Cai (Alibaba Group) · Longbo Huang (IIIS, Tsinghua Univeristy)

LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond
Wenbo Li (Chinese University of Hong Kong) · Kun Zhou (Shenzhen SmartMore Technology Co., Ltd.) · Lu Qi (The Chinese University of Hong Kong) · Nianjuan Jiang (Shenzhen SmartMore Technology Co., Ltd.) · Jiangbo Lu (Shenzhen SmartMore Technology Co., Ltd.) · Jiaya Jia (CUHK)

Unfolding the Alternating Optimization for Blind Super Resolution
zhengxiong luo (中国科学院自动化所) · Yan Huang (CRIPAC, CASIA) · Shang Li (CASIA) · Liang Wang (NLPR, China) · Tieniu Tan (Chinese Academy of Sciences)

Assessing SATNet's Ability to Solve the Symbol Grounding Problem
Oscar Chang (Columbia University) · Lampros Flokas (Columbia University) · Hod Lipson (Columbia University) · Michael Spranger (Sony)

Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation
Bowen Li (University of Oxford) · Xiaojuan Qi (The University of Hong Kong) · Philip Torr (University of Oxford) · Thomas Lukasiewicz (University of Oxford)

Practical Quasi-Newton Methods for Training Deep Neural Networks
Donald Goldfarb (Columbia University) · Yi Ren (Columbia University) · Achraf Bahamou (Columbia University)

RANet: Region Attention Network for Semantic Segmentation
Dingguo Shen (Shenzhen University) · Yuanfeng Ji (City University of Hong Kong) · Ping Li (The Hong Kong Polytechnic University) · Yi Wang (Shenzhen University) · Di Lin (Tianjin University)

HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory
Jie Ren (University of California, Merced) · Minjia Zhang (Microsoft) · Dong Li (University of California, Merced)

Online Adaptation for Consistent Mesh Reconstruction in the Wild
Xueting Li (University of California, Merced) · Sifei Liu (NVIDIA) · Shalini De Mello (NVIDIA) · Kihwan Kim (NVIDIA) · Xiaolong Wang (UCSD/UC Berkeley) · Ming-Hsuan Yang (Google / UC Merced) · Jan Kautz (NVIDIA)

Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs
Naganand Yadati (Indian Institute of Science)

PIE-NET: Parametric Inference of Point Cloud Edges
Xiaogang Wang (Beihang University) · Yuelang Xu (Tsinghua University) · Kai Xu (National University of Defense Technology) · Andrea Tagliasacchi (Google Research, Brain) · Bin Zhou (State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering,Beihang University) · Ali Mahdavi-Amiri (Simon Fraser University) · Hao Zhang (Simon Fraser University)

Kernel Based Progressive Distillation for Adder Neural Networks
Yixing Xu (Huawei Noah's Ark Lab) · Chang Xu (University of Sydney) · Xinghao Chen (Huawei Noah's Ark Lab) · Wei Zhang (Noah's Ark Lab, Huawei Inc.) · Chunjing XU (Huawei Technologies) · Yunhe Wang (Huawei Noah's Ark Lab)

Self-Supervised Learning by Cross-Modal Audio-Video Clustering
Humam Alwassel (KAUST) · Dhruv Mahajan (Facebook) · Bruno Korbar (Facebook) · Lorenzo Torresani (Facebook AI) · Bernard Ghanem (KAUST) · Du Tran (Facebook AI)

Statistical and Topological Properties of Sliced Probability Divergences
Kimia Nadjahi (Télécom ParisTech) · Alain Durmus (ENS Paris Saclay) · Lénaïc Chizat (CNRS) · Soheil Kolouri (HRL Laboratories LLC) · Shahin Shahrampour (Texas A&M University) · Umut Simsekli (Institut Polytechnique de Paris/ University of Oxford)

UWSOD: Toward Fully-Supervised-Level Performance Weakly Supervised Object Detection
Yunhang Shen (Xiamen University) · Rongrong Ji (Xiamen University, China) · Zhiwei Chen (Xiamen University) · Yongjian Wu (Tencent Technology (Shanghai) Co.,Ltd) · Feiyue Huang (Tencent)

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
Gen Li (Tsinghua University) · Yuting Wei (Carnegie Mellon University) · Yuejie Chi (CMU) · Yuantao Gu (Tsinghua University) · Yuxin Chen (Princeton University)

Task-Robust Model-Agnostic Meta-Learning
Liam Collins (University of Texas at Austin) · Aryan Mokhtari (UT Austin) · Sanjay Shakkottai (University of Texas at Austin)

Efficient Projection-free Algorithms for Saddle Point Problems
Cheng Chen (Shanghai Jiao Tong University) · Luo Luo (The Hong Kong University of Science and Technology) · Weinan Zhang (Shanghai Jiao Tong University) · Yong Yu (Shanghai Jiao Tong Unviersity)

Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
Jing Xu (Peking University) · Fangwei Zhong (Peking University) · Yizhou Wang (Peking University)

Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate
Akifumi Okuno (The Institute of Statistical Mathematics) · Hidetoshi Shimodaira (Kyoto University / RIKEN AIP)

The Diversified Ensemble Neural Network
Shaofeng Zhang (University of Electronic Science and Technology of China) · Meng Liu ( university of electronic science and technology of china) · Junchi Yan (Shanghai Jiao Tong University)

Searching for Low-Bit Weights in Quantized Neural Networks
zhaohui yang (peking university) · Yunhe Wang (Huawei Noah's Ark Lab) · Kai Han (Huawei Noah's Ark Lab) · Chunjing XU (Huawei Technologies) · Chao Xu (Peking University) · Dacheng Tao (University of Sydney) · Chang Xu (University of Sydney)

Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks
Dongqi Han (Okinawa Institute of Science and Technology) · Erik De Schutter (OIST) · Sungho Hong (Okinawa Institute of Science and Technology)

Deep Diffusion-Invariant Wasserstein Distributional Classification
Sung Woo Park+ (Chung-Ang University) · Dong Wook Shu (Chung-Ang Univ., Korea) · Junseok Kwon (Chung-Ang Univ., Korea)

The Generalized Lasso with Nonlinear Observations and Generative Priors
Zhaoqiang Liu (National University of Singapore) · Jonathan Scarlett (National University of Singapore)

Learning to Learn Variational Semantic Memory
Xiantong Zhen (University of Amsterdam) · Yingjun Du (University of Amsterdam) · Huan Xiong (Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)) · Qiang Qiu (Purdue University) · Cees Snoek (University of Amsterdam) · Ling Shao (Inception Institute of Artificial Intelligence)

Consistent Structural Relation Learning for Zero-Shot Segmentation
Peike Li (University of Technology Sydney) · Yunchao Wei (UTS) · Yi Yang (UTS)

Submodular Maximization Through Barrier Functions
Ashwinkumar Badanidiyuru (Google Research) · Amin Karbasi (Yale) · Ehsan Kazemi (Google) · Jan Vondrak (Stanford University)

Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks
Ioana Bica (University of Oxford) · James Jordon (University of Oxford) · Mihaela van der Schaar (University of Cambridge)

Learning Loss for Test-Time Augmentation
Ildoo Kim (Kakao Brain) · Younghoon Kim (Sungshin Women's University) · Sungwoong Kim (Kakao Brain)

The Pitfalls of Simplicity Bias in Neural Networks
Harshay Shah (Microsoft Research) · Kaustav Tamuly (Microsoft Research) · Aditi Raghunathan (Stanford University) · Prateek Jain (Microsoft Research) · Praneeth Netrapalli (Microsoft Research)

Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe
Kiran Thekumparampil (Univ. of Illinois at Urbana-Champaign) · Prateek Jain (Microsoft Research) · Praneeth Netrapalli (Microsoft Research) · Sewoong Oh (University of Washington)

Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
Dheeraj Nagaraj (Massachusetts Institute of Technology) · Xian Wu (Stanford University) · Guy Bresler (MIT) · Prateek Jain (Microsoft Research) · Praneeth Netrapalli (Microsoft Research)

Differentially-Private Federated Contextual Bandits
Abhimanyu Dubey (MIT) · Alex `Sandy' Pentland (MIT)

Off-Policy Imitation Learning from Observations
Zhuangdi Zhu (Michigan State University) · Kaixiang Lin (Michigan State University) · Bo Dai (Google Brain) · Jiayu Zhou (Michigan State University)

Neural Non-Rigid Tracking
Aljaz Bozic (Technical University Munich) · Pablo Palafox (Technical University Munich) · Michael Zollhöfer (Stanford University) · Angela Dai (Technical University of Munich) · Justus Thies (Technical University of Munich) · Matthias Niessner (Technical University of Munich)

Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping
Minjia Zhang (Microsoft) · Yuxiong He (Microsoft)

Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring
Jiangxin Dong (Max Planck Institute for Informatics) · Stefan Roth (TU Darmstadt) · Bernt Schiele (Max Planck Institute for Informatics)

AdaTune: Adaptive Tensor Program Compilation Made Efficient
Menghao Li (Microsoft) · Minjia Zhang (Microsoft) · Chi Wang (Microsoft Research) · Mingqin Li (Microsoft)

Texture Interpolation for Probing Visual Perception
Jonathan Vacher (Albert Einstein College of Medicine) · Aida Davila (Albert Einstein College of Medicine) · Adam Kohn (Albert Einstein College of Medicine) · Ruben Coen-Cagli (Albert Einstein College of Medicine)

Stable and expressive recurrent vision models
Drew Linsley (Brown University) · Alekh Karkada Ashok (Brown University) · Lakshmi Narasimhan Govindarajan (Brown University) · Rex Liu (Brown University) · Thomas Serre (Brown University)

Deep reconstruction of strange attractors from time series
William Gilpin (Harvard University)

SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images
Chen-Hsuan Lin (Carnegie Mellon University) · Chaoyang Wang (Carnegie Mellon University) · Simon Lucey (CMU)

Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation
Zhiwei Deng (Princeton University) · Karthik Narasimhan (Princeton University) · Olga Russakovsky (Princeton University)

Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?
Vitaly Kurin (University of Oxford) · Saad Godil (NVIDIA) · Shimon Whiteson (University of Oxford) · Bryan Catanzaro (NVIDIA)

Calibratable Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
Haotao Wang (University of Texas at Austin) · Tianlong Chen (Unversity of Texas at Austin) · Shupeng Gui (University of Rochester) · TingKuei Hu (Texas A&M University) · Ji Liu (Kwai Inc.) · Zhangyang Wang (University of Texas at Austin)

Adaptive Gradient Quantization for Data-Parallel SGD
Fartash Faghri (University of Toronto) · Iman Tabrizian (University of Toronto) · Ilia Markov (IST Austria) · Dan Alistarh (IST Austria & Neural Magic Inc.) · Daniel Roy (Univ of Toronto & Vector) · Ali Ramezani-Kebrya (Vector Institute)

Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Ruizhi Deng (Simon Fraser University) · Bo Chang (Borealis AI) · Marcus Brubaker (Borealis AI) · Greg Mori (Borealis AI) · Andreas Lehrmann (Borealis AI)

Implicit Neural Representations with Periodic Activation Functions
Vincent Sitzmann (Stanford University) · Julien Martel (Stanford University) · Alexander Bergman (Stanford University) · David Lindell (Stanford University) · Gordon Wetzstein (Stanford University)

Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
Takeshi Teshima (The University of Tokyo) · Isao Ishikawa (Ehime University) · Koichi Tojo (RIKEN AIP) · Kenta Oono (The University of Tokyo, Preferred Networks Inc.) · Masahiro Ikeda (RIKEN AIP) · Masashi Sugiyama (RIKEN / University of Tokyo)

MetaSDF: Meta-Learning Signed Distance Functions
Vincent Sitzmann (Stanford University) · Eric Chan (Stanford University) · Richard Tucker (Google) · Noah Snavely (Cornell University and Google AI) · Gordon Wetzstein (Stanford University)

Graph Cross Networks with Vertex Infomax Pooling
Maosen Li (Shanghai Jiao Tong University) · Siheng Chen (MERL) · Ya Zhang (Cooperative Medianet Innovation Center, Shang hai Jiao Tong University) · Ivor Tsang (University of Technology, Sydney)

On the Trade-off between Adversarial and Backdoor Robustness
Cheng-Hsin Weng (National Tsing Hua University) · Yan-Ting Lee (National Tsing Hua University) · Shan-Hung (Brandon) Wu (National Tsing Hua University)

Dense Feature Composition for Zero-Shot Learning
Dat Huynh (Northeastern University) · Ehsan Elhamifar (Northeastern University)

Gradient Surgery for Multi-Task Learning
Tianhe Yu (Stanford University) · Saurabh Kumar (Stanford University) · Abhishek Gupta (UC Berkeley) · Sergey Levine (UC Berkeley) · Karol Hausman (Google Brain) · Chelsea Finn (Stanford)

Federated Principal Component Analysis.
Andreas Grammenos (University of Cambridge) · Rodrigo Mendoza Smith (Quine Technologies) · Jon Crowcroft (University of Cambridge) · Cecilia Mascolo (University of Cambridge)

Fairness constraints can help exact inference in structured prediction
Kevin Bello (Purdue University) · Jean Honorio (Purdue University)

Proximity Operator of the Matrix Perspective Function and its Applications
Joong-Ho Won (Seoul National University)

A Class of Algorithms for General Instrumental Variable Models
Niki Kilbertus (Helmholtz AI) · Matt Kusner (University College London) · Ricardo Silva (University College London)

CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching
Zeping Yu (Tencent Security Keen Lab) · Wenxin Zheng (Tencent Keen Lab, Shanghai Jiao Tong University) · Jiaqi Wang (Tencent Keen Lab) · Qiyi Tang (Tencent Keen Lab) · Sen Nie (Tencent Keen Lab) · Shi Wu (Tencent Keen Lab)

Dual-Free Stochastic Decentralized Optimization with Variance Reduction
Hadrien Hendrikx (INRIA - PSL) · Francis Bach (INRIA - Ecole Normale Superieure) · Laurent Massoulié (Inria)

Backpropagating Linearly Improves Transferability of Adversarial Examples
Yiwen Guo (ByteDance AI Lab) · Qizhang Li (ByteDance AI Lab) · Hao Chen (UC Davis)

Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms
Sascha Saralajew (Dr. Ing. h.c. F. Porsche AG) · Lars Holdijk (University of Amsterdam) · Thomas Villmann (University of Applied Sciences Mittweida)

Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework
Dinghuai Zhang (Peking University) · Mao Ye (The University of Texas at Austin) · Chengyue Gong (Peking University) · Zhanxing Zhu (Peking University) · Qiang Liu (UT Austin)

The Strong Screening Rule for SLOPE
Johan Larsson (Lund University) · Malgorzata Bogdan (University of Wroclaw) · Jonas Wallin (Lund university)

Content Provider Dynamics and Coordination in Recommendation Ecosystems
Omer Ben-Porat (Technion – Israel Institute of Technology) · Itay Rosenberg (Technion) · Moshe Tennenholtz (Technion--Israel Institute of Technology)

DISK: Learning local features with policy gradient
Michał Tyszkiewicz (EPFL) · Pascal Fua (EPFL, Switzerland) · Eduard Trulls (Google)

Learning Individually Inferred Communication for Multi-Agent Cooperation
Ziluo Ding (Peking University) · Tiejun Huang (Peking University) · Zongqing Lu (Peking University)

Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting
Jorge Mendez (University of Pennsylvania) · Boyu Wang (University of Western Ontario) · Eric Eaton (University of Pennsylvania)

Hard Negative Mixing for Contrastive Learning
Yannis Kalantidis (NAVER LABS Europe) · Mert B Sariyildiz (Bilkent University) · Noe Pion (NAVER Labs Europe) · Philippe Weinzaepfel (NAVER LABS Europe) · Diane Larlus (Naver Labs Europe)

Robust Quantization: One Model to Rule Them All
Moran Shkolnik (Intel) · Brian Chmiel (Intel) · Ron Banner (Intel - Artificial Intelligence Products Group (AIPG)) · Gil Shomron (Technion - Israel Institute of Technology) · Yury Nahshan (Intel - Artificial Intelligence Products Group (AIPG)) · Alex Bronstein (Technion) · Uri Weiser (Technion - Israel Institute of Technology)

Projection Robust Wasserstein Distance and Riemannian Optimization
Tianyi Lin (UC Berkeley) · Chenyou Fan (The Chinese University of Hong Kong, Shenzhen) · Nhat Ho (University of Texas at Austin) · Marco Cuturi (Google Brain & CREST - ENSAE) · Michael Jordan (UC Berkeley)

Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm
Tianyi Lin (UC Berkeley) · Nhat Ho (University of Texas at Austin) · Xi Chen (New York University) · Marco Cuturi (Google Brain & CREST - ENSAE) · Michael Jordan (UC Berkeley)

Implicit Rank-Minimizing Autoencoder
Li Jing (Facebook AI Research) · Jure Zbontar (Facebook) · yann lecun (Facebook)

Exponential ergodicity of mirror-Langevin diffusions
Sinho Chewi (Massachusetts Institute of Technology) · Thibaut Le Gouic (Massachusetts Institute of Technology) · Chen Lu (Massachusetts Institute of Technology) · Tyler Maunu (Massachusetts Institute of Technology) · Philippe Rigollet (MIT) · Austin J Stromme (MIT)

Deep Reinforcement and InfoMax Learning
Bogdan Mazoure (McGill University) · Remi Tachet des Combes (Microsoft Research Montreal) · Thang Long DOAN (McGill) · Philip Bachman (Microsoft Research) · R Devon Hjelm (Microsoft Research)

A Group-Theoretic Framework for Data Augmentation
Shuxiao Chen (University of Pennsylvania) · Edgar Dobriban (University of Pennsylvania) · Jane Lee (University of Pennsylvania)

Approximation Based Variance Reduction for Reparameterization Gradients
Tomas Geffner (UMass Amherst) · Justin Domke (University of Massachusetts, Amherst)

Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu (Stanford University) · Matthias Fey (TU Dortmund University) · Marinka Zitnik (Harvard University) · Yuxiao Dong (Microsoft) · Hongyu Ren (Stanford University) · Bowen Liu (Stanford University) · Michele Catasta (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Massimo Caccia (MILA) · Pau Rodriguez (CVC) · Oleksiy Ostapenko (University of Montreal, MILA) · Fabrice Normandin (MILA) · Min Lin (MILA) · Lucas Page-Caccia (McGill University) · Issam Hadj Laradji (University of British Columbia) · Irina Rish (Mila/UdeM) · Alexandre Lacoste (Element AI) · David Vázquez (Element AI) · Laurent Charlin (MILA / U.Montreal)

Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition
Zijun Cui (Rensselaer Polytechnic Institute) · Tengfei Song (Southeast University) · Yuru Wang (Northeast Normal University) · Qiang Ji (Rensselaer Polytechnic Institute)

Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards
Yijie Guo (University of Michigan) · Jongwook Choi (University of Michigan) · Marcin Moczulski (Google Brain) · Shengyu Feng (University of Illinois Urbana Champaign) · Samy Bengio (Google Research, Brain Team) · Mohammad Norouzi (Google Brain) · Honglak Lee (Google / U. Michigan)

Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity
Simon Du (Institute for Advanced Study) · Jason Lee (Princeton University) · Gaurav Mahajan (University of California, San Diego) · Ruosong Wang (Carnegie Mellon University)

Swapping Autoencoder for Deep Image Manipulation
Taesung Park (UC Berkeley) · Jun-Yan Zhu (Adobe, CMU) · Oliver Wang (Adobe Research) · Jingwan Lu (Adobe Research) · Eli Shechtman (Adobe Research, US) · Alexei Efros (UC Berkeley) · Richard Zhang (Adobe)

ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
Yibo Yang (Peking University) · Hongyang Li (Peking University) · Shan You (SenseTime) · Fei Wang (SenseTime) · Chen Qian (SenseTime) · Zhouchen Lin (Peking University)

Universal Domain Adaptation through Self Supervision
Kuniaki Saito (Boston University) · Donghyun Kim (Boston University) · Stan Sclaroff (Boston University) · Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)

OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification
Taewon Jeong (KAIST) · Heeyoung Kim (KAIST)

Auxiliary Task Reweighting for Minimum-data Learning
Baifeng Shi (Peking University) · Judy Hoffman (Georgia Institute of Technology) · Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research) · Trevor Darrell (UC Berkeley) · Huijuan Xu (University of California, Berkeley)

Theory-Inspired Path-Regularized Differential Network Architecture Search
Pan Zhou (Salesforce) · Caiming Xiong (Salesforce) · Richard Socher (Salesforce) · Steven Chu Hong Hoi (Salesforce)

Compositional Generalization by Learning Analytical Expressions
Qian Liu (Beihang University) · Shengnan An (Xi’an Jiaotong University) · Jian-Guang Lou (Microsoft) · Bei Chen (Microsoft Research Asia) · Zeqi Lin (Microsoft) · Yan Gao (Microsoft Research Asia, Beijing, China) · Bin Zhou (State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering,Beihang University) · Nanning Zheng (Xi'an Jiaotong University) · Dongmei Zhang (Microsoft Research)

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Mathilde Caron (INRIA / FAIR) · Ishan Misra (Facebook AI Research ) · Julien Mairal (Inria) · Priya Goyal (Facebook AI Research) · Piotr Bojanowski (Facebook) · Armand Joulin (Facebook AI research)

Blind Video Temporal Consistency via Deep Video Prior
Chenyang Lei (HKUST) · Yazhou Xing (HKUST) · Qifeng Chen (HKUST)

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
Yu Yao (University of Sydney) · Tongliang Liu (The University of Sydney) · Bo Han (HKBU / RIKEN) · Mingming Gong (University of Melbourne) · Jiankang Deng (Imperial College London) · Gang Niu (RIKEN) · Masashi Sugiyama (RIKEN / University of Tokyo)

A mathematical model for automatic differentiation in machine learning
Jérôme Bolte (Université Toulouse 1) · Edouard Pauwels (IRIT)

Cross-scale Internal Graph Convolution Network for Image Super-Resolution
Shangchen Zhou (Nanyang Technological University) · Jiawei Zhang (Sensetime Research) · Wangmeng Zuo (Harbin Institute of Technology) · Chen Change Loy (Nanyang Technological University)

No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix
Emmanouil-Vasileios Vlatakis-Gkaragkounis (Columbia University) · Lampros Flokas (Columbia University) · Thanasis Lianeas (National Technical University of Athens) · Panayotis Mertikopoulos (CNRS (French National Center for Scientific Research)) · Georgios Piliouras (Singapore University of Technology and Design)

LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration
Bharat Lal Bhatnagar (MPI-INF) · Cristian Sminchisescu (Google Research) · Christian Theobalt (MPI Informatik) · Gerard Pons-Moll (MPII, Germany)

Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
Jinheon Baek (KAIST) · Dong Bok Lee (KAIST) · Sung Ju Hwang (KAIST, AITRICS)

Near-Optimal Comparison Based Clustering
Michaël Perrot (Max Planck Institute for Intelligent Systems) · Pascal Esser (Technical University of Munich) · Debarghya Ghoshdastidar (Technical University Munich)

Active Invariant Causal Prediction: Experiment Selection through Stability
Juan Gamella (ETH Zürich) · Christina Heinze-Deml (ETH Zurich)

Neural Unsigned Distance Fields for Implicit Function Learning
Julian Chibane (Max Planck Institute for Informatics, University of Wuerzburg) · Mohamad Aymen mir (MPI Informatics, Saarland) · Gerard Pons-Moll (MPII, Germany)

BrainProp: How the brain can implement reward-based error backpropagation
Isabella Pozzi (Centrum Wiskunde & Informatica) · Sander Bohte (CWI) · Pieter Roelfsema (Netherlands Institute for Neuroscience)

Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition
Zihan Zhang (Tsinghua University) · Yuan Zhou (UIUC) · Xiangyang Ji (Tsinghua University)

Robust large-margin learning in hyperbolic space
Melanie Weber (Princeton University) · Manzil Zaheer (Google Research) · Ankit Singh Rawat (Google Research) · Aditya Menon (Google) · Sanjiv Kumar (Google Research)

Adversarial Bandits with Corruptions: Regret Lower Bound and No-regret Algorithm
lin yang (UMass) · Mohammad Hajiesmaili (UMass Amherst) · Mohammad Sadegh Talebi (University of Copenhagen) · John C. S. Lui (The Chinese University of Hong Kong) · Wing Shing Wong (The Chinese University of Hong Kong)

Adaptive Online Estimation of Piecewise Polynomial Trends
Dheeraj Baby (UC Santa Barbara) · Yu-Xiang Wang (UC Santa Barbara)

A Dictionary Approach to Domain-Invariant Learning in Deep Networks
Ze Wang (Duke University) · Xiuyuan Cheng (Duke University) · Guillermo Sapiro (Duke University) · Qiang Qiu (Purdue University)

Parts-dependent Label Noise: Towards Instance-dependent Label Noise
Xiaobo Xia (The University of Sydney / Xidian University) · Tongliang Liu (The University of Sydney) · Bo Han (HKBU / RIKEN) · Nannan Wang (Xidian University) · Mingming Gong (University of Melbourne) · Haifeng Liu (Brain-Inspired Technology Co., Ltd.) · Gang Niu (RIKEN) · Dacheng Tao (University of Sydney) · Masashi Sugiyama (RIKEN / University of Tokyo)

Transferable Calibration with Lower Bias and Variance in Domain Adaptation
Ximei Wang (Tsinghua University) · Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University) · Michael Jordan (UC Berkeley)

TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation
DONGXU LI (THE AUSTRALIAN NATIONAL UNIVERSITY) · Chenchen Xu (The Australian National University) · Xin Yu (University of Technology Sydney) · Kaihao Zhang (Australian National University) · Benjamin Swift (Australian National University) · Hanna Suominen (The Australian National University and Data61/CSIRO) · Hongdong Li (Australian National University)

SGD with shuffling: optimal rates without component convexity and large epoch requirements
Kwangjun Ahn (MIT) · Chulhee Yun (MIT) · Suvrit Sra (MIT)

Concentration Bounds for Co-occurrence Matrices of Markov Chains
Jiezhong Qiu (Tsinghua University) · Chi Wang (Microsoft Research) · Ben Liao (Tencent) · Richard Peng (Georgia Tech) · Jie Tang (Tsinghua University)

A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
Haoran Zhu (University of Wisconsin-Madison) · Pavankumar Murali (IBM) · Dzung Phan (IBM Research, T. J. Watson Research Center) · Lam Nguyen (IBM Research, Thomas J. Watson Research Center) · Jayant Kalagnanam (IBM Research)

Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits
Siwei Wang (IIIS, Tsinghua University) · Longbo Huang (IIIS, Tsinghua Univeristy) · John C. S. Lui (The Chinese University of Hong Kong)

Meta-Neighborhoods
Siyuan Shan (UNC Chapel Hill) · Yang Li (UNC-Chapel Hill) · Junier Oliva (UNC - Chapel Hill)

ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
Wen-Da Jin (Tianjin University) · Jun Xu (Nankai University) · Ming-Ming Cheng (Nankai University) · Yi Zhang (Tianjin University) · Wei Guo (Tianjin University)

Provably Consistent Partial-Label Learning
Lei Feng (Nanyang Technological University) · Jiaqi Lv (Southeast University) · Bo Han (HKBU / RIKEN) · Miao Xu (RIKEN AIP) · Gang Niu (RIKEN) · Xin Geng (Southeast University) · Bo An (Nanyang Technological University) · Masashi Sugiyama (RIKEN / University of Tokyo)

Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
Yujing Hu (NetEase Fuxi AI Lab) · Weixun Wang (Tianjin University) · Hangtian Jia (Netease Fuxi AI Lab) · Yixiang Wang (University of Science and Technology of China) · Yingfeng Chen (NetEase Fuxi AI Lab) · Jianye Hao (Tianjin University) · Feng Wu (University of Science and Technology of China) · Changjie Fan (NetEase Fuxi AI Lab)

Hierarchical Quantized Autoencoders
Will Williams (Speechmatics) · Sam Ringer (Speechmatics) · Tom Ash (Speechmatics) · David MacLeod (Speechmatics) · Jamie Dougherty (Speechmatics) · John Hughes (Speechmatics)

Sharper Generalization Bounds for Pairwise Learning
Yunwen Lei (University of Birmingham) · Antoine Ledent (TU Kaiserslautern) · Marius Kloft (TU Kaiserslautern)

Incorporating BERT into Parallel Sequence Decoding with Adapters
Junliang Guo (University of Science and Technology of China) · Zhirui Zhang (Alibaba Group Inc.) · Linli Xu (University of Science and Technology China) · Hao-Ran Wei (Alibaba DAMO Academy) · Boxing Chen (Alibaba Group) · Enhong Chen (University of Science and Technology of China)

Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
Tao Han (Northwestern Polytechnical University) · Junyu Gao (Northwestern Polytechnical University, Center for OPTical IMagery Analysis and Learning) · Yuan Yuan (Northwestern Polytechnical University) · Qi Wang (Northwestern Polytechnical University)

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
Ilyes Khemakhem (UCL) · Ricardo Monti (UCL) · Diederik P. Kingma (Google) · Aapo Hyvarinen (University of Helsinki)

Effective Diversity in Population Based Reinforcement Learning
Jack Parker-Holder (University of Oxford) · Aldo Pacchiano (UC Berkeley) · Krzysztof M Choromanski (Google Brain Robotics) · Stephen J Roberts (University of Oxford)

RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
Sebastien Ehrhardt (University of Oxford) · Oliver Groth (Oxford Robotics Institute) · Aron Monszpart (Niantic) · Martin Engelcke (University of Oxford) · Ingmar Posner (Oxford University) · Niloy Mitra (UCL/Adobe) · Andrea Vedaldi (Facebook AI Research and University of Oxford)

Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits
Jack Parker-Holder (University of Oxford) · Vu Nguyen (University of Oxford) · Stephen J Roberts (University of Oxford)

Stochastic Normalization
Zhi Kou (Tsinghua University) · Kaichao You (Tsinghua University) · Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University)

A Boolean Task Algebra for Reinforcement Learning
Geraud Nangue Tasse (University of the Witwatersrand) · Steven James (University of the Witwatersrand) · Benjamin Rosman (University of the Witwatersrand / CSIR)

Consistent Hierarchical Multi-Label Classification Networks
Eleonora Giunchiglia (University of Oxford) · Thomas Lukasiewicz (University of Oxford)

Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms
Mahdi Haghifam (University of Toronto) · Jeffrey Negrea (University of Toronto) · Ashish Khisti (University of Toronto) · Daniel Roy (Univ of Toronto & Vector) · Gintare Karolina Dziugaite (Element AI)

A new convergent variant of Q-learning with linear function approximation
Diogo Carvalho (Instituto Superior Técnico & INESC-ID) · Francisco S. Melo (IST/INESC-ID) · Pedro A. Santos (Instituto Superior Técnico)

Learning Disentangled Representations and Group Structure of Dynamical Environments
Robin Quessard (indust.ai) · Thomas Barrett (University of Oxford) · William Clements (indust.ai)

In search of robust measures of generalization
Gintare Karolina Dziugaite (Element AI) · Alexandre Drouin (Element AI) · Brady Neal (Mila) · Nitarshan Rajkumar (Mila, Université de Montréal) · Ethan Caballero (Mila) · Linbo Wang (University of Toronto) · Ioannis Mitliagkas (Mila & University of Montreal) · Daniel Roy (Univ of Toronto & Vector)

Continuous Regularized Wasserstein Barycenters
Lingxiao Li (MIT) · Aude Genevay (MIT) · Mikhail Yurochkin (IBM Research, MIT-IBM Watson AI Lab) · Justin M Solomon (MIT)

Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning
Runzhong Wang (Shanghai Jiao Tong University) · Junchi Yan (Shanghai Jiao Tong University) · Xiaokang Yang (Shanghai Jiao Tong University)

Avoiding Side Effects in Complex Environments
Alex Turner (Oregon State University) · Neale Ratzlaff (Oregon State University) · Prasad Tadepalli (Oregon State University)

Adversarial Weight Perturbation Improves Adversarial Training
Dongxian Wu (Tsinghua University) · Yisen Wang (Peking University) · Shu-Tao Xia (Tsinghua University)

Self-Supervised Generative Adversarial Compression
Chong Yu (NVIDIA) · Chong Yu (Intel)

Permute-and-Flip: A new mechanism for differentially-private selection
Ryan McKenna (University of Massachusetts, Amherst) · Daniel Sheldon (University of Massachusetts Amherst)

SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
Fabian Fuchs (University of Oxford) · Daniel Worrall (University of Amsterdam) · Volker Fischer (Robert Bosch GmbH, Bosch Center for Artificial Intelligence) · Max Welling (University of Amsterdam / Qualcomm AI Research)

Adaptive Reduced Rank Regression
Qiong Wu (College of William and Mary) · Felix MF Wong (Google) · Yanhua Li ("Worcester Polytechnic Institute, USA") · Zhenming Liu (William and Mary) · Varun Kanade (University of Oxford)

Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control
Zhiyuan Xu (Syracuse University) · Kun Wu (Syracuse University) · Zhengping Che (DiDi AI Labs, Didi Chuxing) · Jian Tang (DiDi AI Labs, DiDi Chuxing) · Jieping Ye (Didi Chuxing)

Learning Deformable Tetrahedral Meshes for 3D Reconstruction
Jun Gao (University of Toronto) · Wenzheng Chen (University of Toronto) · Tommy Xiang (University of Toronto) · Alec Jacobson (University of Toronto) · Morgan McGuire (NVIDIA) · Sanja Fidler (University of Toronto)

Calibrating CNNs for Lifelong Learning
Pravendra Singh (Indian Institute of Technology Kanpur) · Vinay Kumar Verma (Indian Institute of Technology Kanpur) · Pratik Mazumder (Indian Institute of Technology, Kanpur) · Lawrence Carin (Duke University) · Piyush Rai (IIT Kanpur)

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Xiang Li (NJUST) · Wenhai Wang (Nanjing University) · Lijun Wu (Sun Yat-sen University) · Shuo Chen (Nanjing University of Science and Technology) · Xiaolin Hu (Tsinghua University) · Jun Li (Nanjing University of Science and Technology) · Jinhui Tang (Nanjing University of Science and Technology) · Jian Yang (Nanjing University of Science and Technology)

Provable Overlapping Community Detection in Weighted Graphs
Jimit Majmudar (University of Waterloo) · Stephen Vavasis (University of Waterloo )

GAN Memory with No Forgetting
Yulai Cong (Duke University) · Miaoyun Zhao (Duke University) · Jianqiao Li (Duke University) · Sijia Wang (Duke University) · Lawrence Carin (Duke University)

Learning Black-Box Attackers with Transferable Priors and Query Feedback
Jiancheng YANG (Shanghai Jiao Tong University) · Yangzhou Jiang (Shanghai Jiaotong University) · Xiaoyang Huang (Shanghai Jiao Tong University) · Bingbing Ni (Shanghai Jiao Tong University) · Chenglong Zhao (Shanghai Jiao Tong University)

Hybrid Models for Learning to Branch
Prateek Gupta (University of Oxford) · Maxime Gasse (Polytechnique Montréal) · Elias Khalil (University of Toronto) · Pawan K Mudigonda (University of Oxford) · Andrea Lodi (École Polytechnique Montréal) · Yoshua Bengio (Mila / U. Montreal)

Adversarial Self-Supervised Contrastive Learning
Minseon Kim (KAIST) · Jihoon Tack (KAIST) · Sung Ju Hwang (KAIST, AITRICS)

The Power of Predictions in Online Control
Chenkai Yu (Tsinghua University) · Guanya Shi (Caltech) · Soon-Jo Chung (Caltech) · Yisong Yue (Caltech) · Adam Wierman (California Institute of Technology)

Multi-task Batch Reinforcement Learning with Metric Learning
Jiachen Li (University of California, San Diego) · Quan Vuong (University of California San Diego) · Shuang Liu (University of California, San Diego) · Minghua Liu (UCSD) · Kamil Ciosek (Microsoft) · Henrik Christensen (UC San Diego) · Hao Su (UCSD)

Coresets via Bilevel Optimization for Continual Learning and Streaming
Zalán Borsos (ETH Zurich) · Mojmir Mutny (ETH Zurich) · Andreas Krause (ETH Zurich)

Self-Adaptive Training: beyond Empirical Risk Minimization
Lang Huang (Peking University) · Chao Zhang (Peking University) · Hongyang Zhang (TTIC)

Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization
Haoliang Li (Nanyang Technological University) · Yufei Wang (Nanyang Technological University) · Renjie Wan (Nanyang Technological University) · Shiqi Wang (CityU) · Tie-Qiang Li (Karolinska Institute) · Alex Kot (Nanyang Technological University)

Passport-aware Normalization for Deep Model Protection
Jie Zhang (University of Science and Technology of China) · Dongdong Chen (Microsoft Cloud AI) · Jing Liao (City University of Hong Kong) · Weiming Zhang (University of Science and Technology of China) · Gang Hua (Wormpex AI Research) · Nenghai Yu (University of Science and Technology of China)

Position-based Scaled Gradient for Model Quantization and Sparse Training
Jangho Kim (Seoul National University) · KiYoon Yoo (Seoul National University) · Nojun Kwak (Seoul National University)

GPS-Net: Graph-based Photometric Stereo Network
Zhuokun Yao (Tianjin University) · Kun Li (Tianjin University) · Ying Fu (Beijing Institute of Technology) · Haofeng Hu (Tianjin University) · Boxin Shi (Peking University)

Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search
Houwen Peng (Microsoft Research) · Hao Du (Microsoft Research) · Hongyuan Yu (MSRA) · QI LI (Tsinghua Univeristy) · Jing Liao (City University of Hong Kong) · Jianlong Fu (Microsoft Research)

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures
Julien Launay (LightOn) · Iacopo Poli (LightOn) · François Boniface (LightOn) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL)

Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
Yu-Guan Hsieh (Université Grenoble Alpes / École Normale Supérieure Paris) · Franck Iutzeler (Univ. Grenoble Alpes) · Jérôme Malick (CNRS and LJK) · Panayotis Mertikopoulos (CNRS (French National Center for Scientific Research))

Robust Meta-learning for Mixed Linear Regression with Small Batches
Weihao Kong (Stanford University) · Raghav Somani (University of Washington) · Sham Kakade (University of Washington & Microsoft Research) · Sewoong Oh (University of Washington)

Deep active inference agents using Monte-Carlo methods
Zafeirios Fountas (University College London; Emotech Labs) · Noor Sajid (University College London) · Pedro Mediano (University of Cambridge ) · Karl Friston (University College London)

ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Cher Bass (King's College London) · Mariana da Silva (King's College London) · Carole Sudre (King's College London) · Petru-Daniel Tudosiu (King's College London) · Stephen Smith (FMRIB Centre - University of Oxford) · Emma Robinson (King's College)

Auditing Differentially Private Machine Learning: How Private is Private SGD?
Matthew Jagielski (Northeastern University) · Jonathan Ullman (Northeastern University) · Alina Oprea (Northeastern University)

Dual-Resolution Correspondence Networks
Xinghui Li (University of Oxford) · Kai Han (University of Oxford) · Shuda Li (University of Oxford) · Victor Prisacariu (University of Oxford)

Semialgebraic Optimization for Lipschitz Constants of ReLU Networks
Tong Chen (LAAS-CNRS) · Jean B Lasserre (lasserre@laas.fr) · Victor Magron (LAAS-CNRS) · Edouard Pauwels (IRIT)

Adversarial Training is a Form of Data-dependent Operator Norm Regularization
Kevin Roth (ETH Zurich) · Yannic Kilcher (ETH Zurich) · Thomas Hofmann (ETH Zurich)

Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
Robert Geirhos (University of Tübingen) · Kristof Meding (University of Tübingen) · Felix A. Wichmann (University of Tübingen)

System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina
Cornelius Schröder (University of Tübingen) · David Klindt (University of Tübingen) · Sarah Strauss (University of Tübingen) · Katrin Franke (University of Tübingen) · Matthias Bethge (University of Tübingen) · Thomas Euler (University of Tübingen) · Philipp Berens (University of Tübingen)

Demystifying Orthogonal Monte Carlo and Beyond
Han Lin (Columbia University) · Haoxian Chen (Columbia University) · Krzysztof M Choromanski (Google Brain Robotics) · Tianyi Zhang (Columbia University) · Clement Laroche (Columbia University)

HyNet: Local Descriptor with Hybrid Similarity Measure
Yurun Tian (Imperial College London) · Axel Barroso Laguna (Imperial College London) · Tony Ng (Imperial College London) · Vassileios Balntas (Scape Technologies) · Krystian Mikolajczyk (Imperial College London)

Modeling Shared responses in Neuroimaging Studies through MultiView ICA
Hugo Richard (INRIA) · Luigi Gresele (MPI for Intelligent Systems, Tübingen) · Aapo Hyvarinen (University of Helsinki) · Bertrand Thirion (INRIA) · Alexandre Gramfort (INRIA) · Pierre Ablin (INRIA)

Feature Importance Ranking for Deep Learning
Maksymilian Wojtas (University of Manchester) · Ke Chen (The University of Manchester)

Minimax Bounds for Generalized Linear Models
Kuan-Yun Lee (University of California, Berkeley) · Thomas Courtade (UC Berkeley)

Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
Robin T Schirrmeister (University Medical Center Freiburg) · Yuxuan Zhou (Stuttgart University) · Tonio Ball (Albert-Ludwigs-University) · Dan Zhang (Bosch Center for Artificial Intelligence)

A graph similarity for deep learning
Seongmin Ok (Samsung Advanced Institute of Technology)

Probabilistic Linear Solvers for Machine Learning
Jonathan Wenger (University of Tübingen) · Philipp Hennig (University of Tübingen and MPI for Intelligent Systems Tübingen)

Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals
Ilias Diakonikolas (UW Madison) · Daniel Kane (UCSD) · Nikos Zarifis (University of Wisconsin-Madison)

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn (Google) · David Berthelot (Google Brain) · Nicholas Carlini (Google) · Zizhao Zhang (Google) · Han Zhang (Google) · Colin A Raffel (Google Brain) · Ekin Dogus Cubuk (Google Brain) · Alexey Kurakin (Google Brain) · Chun-Liang Li (Google)

MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
Jeong Un Ryu (KAIST) · JaeWoong Shin (KAIST) · Hae Beom Lee (KAIST) · Sung Ju Hwang (KAIST, AITRICS)

Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
Yaochen Xie (Texas A&M University) · Zhengyang Wang (Texas A&M University) · Shuiwang Ji (Texas A&M University)

Stochastic Variance Reduced Accelerated Dual Averaging for Finite-Sum Optimization
Chaobing Song (Tsinghua University) · Yong Jiang (Tsinghua) · Yi Ma (UC Berkeley)

How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks?
Gaojie Jin (University of Liverpool) · Xinping Yi (University of Liverpool) · Liang Zhang (Institute of Software, Chinese Academy of Sciences) · Lijun Zhang (Institute of Software, Chinese Academy of Sciences) · Sven Schewe (University of Liverpool) · Xiaowei Huang (Liverpool University)

Measuring Robustness to Natural Distribution Shifts in Image Classification
Rohan Taori (University of California, Berkeley) · Achal Dave (Carnegie Mellon University) · Vaishaal Shankar (UC Berkeley) · Nicholas Carlini (Google) · Benjamin Recht (UC Berkeley) · Ludwig Schmidt (UC Berkeley)

Flows for simultaneous manifold learning and density estimation
Johann Brehmer (New York University) · Kyle Cranmer (New York University)

Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso
Jerome-Alexis Chevalier (Inria Saclay Île-de-France) · Joseph Salmon (Université de Montpellier) · Alexandre Gramfort (INRIA) · Bertrand Thirion (INRIA)

On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems
Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Bin Hu (University of Illinois at Urbana-Champaign) · Tamer Basar (University of Illinois at Urbana-Champaign)

Robust compressed sensing of generative models
Ajil Jalal (University of Texas at Austin) · Liu Liu (University of Texas at Austin) · Alexandros Dimakis (University of Texas, Austin) · Constantine Caramanis (UT Austin)

Bayesian Deep Ensembles via the Neural Tangent Kernel
Bobby He (University of Oxford) · Balaji Lakshminarayanan (Google Brain) · Yee Whye Teh (University of Oxford, DeepMind)

Semantic Visual Navigation by Watching YouTube Videos
Matthew Chang (UIUC) · Arjun Gupta (University of Illinois at Urbana-Champaign) · Saurabh Gupta (UIUC)

A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent
Zhenyu Liao (University of California, Berkeley) · Romain Couillet (Université Grenoble Alpes) · Michael W Mahoney (UC Berkeley)

Complementary Attention Self-Distillation for Weakly-Supervised Object Detection
Zeyi Huang (carnegie mellon university) · Yang Zou (Carnegie Mellon University) · B. V. K. Vijaya Kumar (CMU, USA) · Dong Huang (Carnegie Mellon University)

Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation
Jianfeng Zhang (NUS) · Xuecheng Nie (NUS) · Jiashi Feng (National University of Singapore)

Robust Optimization for Fairness with Noisy Protected Groups
Serena Wang (Google) · Wenshuo Guo (UC Berkeley) · Harikrishna Narasimhan (Google Research) · Andrew Cotter (Google) · Maya Gupta (Google) · Michael Jordan (UC Berkeley)

Distributional Robustness with IPMs and links to Regularization and GANs
Hisham Husain (The Australian National University & Data61)

GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification
John Halloran (University of California, Davis) · David M Rocke (University of California, Davis)

One-sample Guided Object Representation Disassembling
Zunlei Feng (Zhejiang University) · Yongming He (Zhejiang University) · Xinchao Wang (Stevens Institute of Technology) · Xin Gao (Alibaba Group) · Jie Lei (Zhejiang University) · Cheng Jin (Fudan University) · Mingli Song (Zhejiang University)

Compositional Generalization via Neural-Symbolic Stack Machines
Xinyun Chen (UC Berkeley) · Chen Liang (Google Brain) · Adams Wei Yu (Google Brain) · Dawn Song (UC Berkeley) · Denny Zhou (Google Brain)

Neural Methods for Point-wise Dependency Estimation
Yao-Hung Hubert Tsai (Carnegie Mellon University) · Han Zhao (Carnegie Mellon University) · Makoto Yamada (Kyoto University/RIKEN AIP) · Louis-Philippe Morency (Carnegie Mellon University) · Russ Salakhutdinov (Carnegie Mellon University)

Latent Template Induction with Gumbel-CRFs
Yao Fu (Columbia University) · Chuanqi Tan (Alibaba Group) · Bin Bi (Alibaba Group) · Mosha Chen (Alibaba Group) · Yansong Feng (Peking University) · Alexander Rush (Cornell University)

Towards Playing Full MOBA Games with Deep Reinforcement Learning
Deheng Ye (Tencent) · Guibin Chen (Tencent) · Wen Zhang (Tencent) · chen sheng (qq) · Bo Yuan (Tencent) · Bo Liu (Tencent) · Jia Chen (Tencent) · Hongsheng Yu (Tencent) · Zhao Liu (Tencent) · Fuhao Qiu (Tencent AI Lab) · Liang Wang (Tencent) · Tengfei Shi (Tencent) · Yinyuting Yin (Tencent) · Bei Shi (Tencent AI Lab) · Lanxiao Huang (Tencent) · qiang fu (Tencent AI Lab) · Wei Yang (Tencent AI Lab) · Wei Liu (Tencent AI Lab)

DFIS: Dynamic and Fast Instance Segmentation
Xinlong Wang (University of Adelaide) · Rufeng Zhang (Tongji University) · Tao Kong (Bytedance) · Lei Li (ByteDance AI Lab) · Chunhua Shen (University of Adelaide)

Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent
Dimitris Fotakis (National Technical University of Athens) · Thanasis Lianeas (National Technical University of Athens) · Georgios Piliouras (Singapore University of Technology and Design) · Stratis Skoulakis (Singapore University of Technology and Design)

Error Bounds of Imitating Policies and Environments
Tian Xu (Nanjing University) · Ziniu Li (Nanjing University) · Yang Yu (Nanjing University)

Learning to Prove Theorems by Learning to Generate Theorems
Mingzhe Wang (Pinceton University) · Jia Deng (Princeton University)

How does this interaction affect me? Interpretable attribution for feature interactions
Michael Tsang (University of Southern California) · Sirisha Rambhatla (University of Southern California) · Yan Liu (University of Southern California)

Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
Amin Jaber (Purdue University) · Murat Kocaoglu (IBM Research) · Karthikeyan Shanmugam (IBM Research, NY) · Elias Bareinboim (Columbia University)

Rethinking Learnable Tree Filter for Generic Feature Transform
Lin Song (Xi'an Jiaotong University) · Yanwei Li (The Chinese University of Hong Kong) · Zhengkai Jiang (Institute of Automation,Chinese Academy of Sciences) · Zeming Li (Megvii(Face++) Inc) · Xiangyu Zhang (MEGVII Technology) · Hongbin Sun (Xi'an Jiaotong University) · Jian Sun (Megvii, Face++) · Nanning Zheng (Xi'an Jiaotong University)

BERT Loses Patience: Fast and Robust Inference with Early Exit
Wangchunshu Zhou (Beihang University) · Canwen Xu (UC San Diego) · Tao Ge (Microsoft Research Asia) · Julian McAuley (UCSD) · Ke Xu (Beihang University) · Furu Wei (Microsoft Research Asia)

Decisions, Counterfactual Explanations and Strategic Behavior
Stratis Tsirtsis (MPI-SWS) · Manuel Gomez Rodriguez (Max Planck Institute for Software Systems)

Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis
Kavi Gupta (UC Berkeley) · Peter Ebert Christensen (Technical University of Denmark) · Xinyun Chen (UC Berkeley) · Dawn Song (UC Berkeley)

Post-training Iterative Hierarchical Data Augmentation for Deep Networks
Adil Khan (Innopolis University) · Khadija Fraz (Hazara University)

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
Miguel Monteiro (Imperial College London) · Loic Le Folgoc (Imperial College London) · Daniel Coelho de Castro (Imperial College London) · Nick Pawlowski (Imperial College London) · Bernardo Marques (Imperial College London) · Konstantinos Kamnitsas (Imperial College London) · Mark van der Wilk (Imperial College) · Ben Glocker (Imperial College London)

MeshSDF: Differentiable Iso-Surface Extraction
Edoardo Remelli (EPFL) · Artem Lukoyanov (EPFL) · Stephan Richter (Intel Labs) · Benoit Guillard (EPFL) · Timur Bagautdinov (Facebook) · Pierre Baque (Neural Concept SA) · Pascal Fua (EPFL, Switzerland)

CogLTX: Applying BERT to Long Texts
Ming Ding (Tsinghua University) · Chang Zhou (Alibaba Group) · Hongxia Yang (Alibaba Group) · Jie Tang (Tsinghua University)

Attribute Prototype Network for Zero-Shot Learning
Wenjia Xu (University of Chinese Academy of Sciences) · Yongqin Xian (Max Planck Institute Informatics) · Jiuniu Wang (City University of Hong Kong) · Bernt Schiele (Max Planck Institute for Informatics) · Zeynep Akata (University of Tübingen)

Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets
Kai Han (Huawei Noah's Ark Lab) · Yunhe Wang (Huawei Noah's Ark Lab) · Qiulin Zhang (Beijing University of Posts and Telecommunications) · Wei Zhang (Noah's Ark Lab, Huawei Inc.) · Chunjing XU (Huawei Technologies) · Tong Zhang (Tencent AI Lab)

SIRI: Spatial Relation Induced Network For Spatial Description Resolution
peiyao wang (ShanghaiTech University) · Weixin Luo (Shanghaitech University) · Yanyu Xu (Shanghaitech University) · Haojie Li (Dalian University of Technology) · Shugong Xu (Shanghai University) · Jianyu Yang (Soochow University) · Shenghua Gao (Shanghaitech University)

Model Agnostic Multilevel Explanations
Karthikeyan Natesan Ramamurthy (IBM Research) · Bhanukiran Vinzamuri (IBM Research) · Yunfeng Zhang (IBM Research) · Amit Dhurandhar (IBM Research)

How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization
Pierluca D'Oro (MILA) · Wojciech Jaśkowski (NNAISENSE SA)

Learning to Adapt to Evolving Domains
Hong Liu (Tsinghua University) · Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University) · Yu Wang (Tsinghua Univ.)

Path Integral Based Convolution and Pooling for Graph Neural Networks
Zheng Ma (Princeton University) · Junyu Xuan (University of Technology Sydney) · Yu Guang Wang (University of New South Wales; MPI MiS) · Ming Li (Zhejiang Normal University) · Pietro Liò (University of Cambridge)

Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
Kiwon Um (Telecom Paris, IP Paris) · Yun (Raymond) Fei (Columbia University) · Philipp Holl (Technical University of Munich) · Robert Brand (Technical University of Munich) · Nils Thuerey (Technical University of Munich)

Universally Quantized Neural Compression
Eirikur Agustsson (Google) · Lucas Theis (Google)

Unsupervised Representation Learning by Invariance Propagation
Feng Wang (Tsinghua University) · Huaping Liu (Tsinghua University) · Di Guo (Tsinghua University) · Sun Fuchun (Tsinghua university)

FedSplit: an algorithmic framework for fast federated optimization
Reese Pathak (University of California, Berkeley) · Martin Wainwright (UC Berkeley)

Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting
Ziping Xu (University of Michigan) · Ambuj Tewari (University of Michigan)

Higher-Order Certification For Randomized Smoothing
Jeet Mohapatra (MIT) · Ching-Yun Ko (MIT) · Tsui-Wei Weng (MIT) · Pin-Yu Chen (IBM Research AI) · Sijia Liu (MIT-IBM Watson AI Lab, IBM Research) · Luca Daniel (Massachusetts Institute of Technology)

Denoising Diffusion Probabilistic Models
Jonathan Ho (UC Berkeley) · Ajay Jain (UC Berkeley) · Pieter Abbeel (UC Berkeley & covariant.ai)

Finding the Homology of Decision Boundaries with Active Learning
Weizhi Li (Arizona State University) · Gautam Dasarathy (Arizona State University) · Karthikeyan Natesan Ramamurthy (IBM Research) · Visar Berisha (Arizona State University)

Ultrahyperbolic Representation Learning
Marc Law (NVIDIA) · Jos Stam (NVIDIA)

Modern Hopfield Networks and Attention for Immune Repertoire Classification
Michael Widrich (LIT AI Lab / University Linz) · Bernhard Schäfl (JKU Linz) · Milena Pavlović (Department of Informatics, University of Oslo) · Hubert Ramsauer (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) · Lukas Gruber (Johannes Kepler University) · Markus Holzleitner (LIT AI Lab / University Linz) · Johannes Brandstetter (LIT AI Lab / University Linz) · Geir Kjetil Sandve (Department of Informatics, University of Oslo) · Victor Greiff (Department of Immunology, University of Oslo) · Sepp Hochreiter (LIT AI Lab / University Linz / IARAI) · Günter Klambauer (LIT AI Lab / University Linz)

HiPPO: Recurrent Memory with Optimal Polynomial Projections
Albert Gu (Stanford) · Tri Dao (Stanford University) · Stefano Ermon (Stanford) · Atri Rudra (University at Buffalo, SUNY) · Christopher Ré (Stanford)

Adversarial Attacks on Linear Contextual Bandits
Evrard Garcelon (Facebook AI Research) · Baptiste Roziere (Facebook AI Research) · Laurent Meunier (Dauphine University - FAIR Paris) · Jean Tarbouriech (Facebook AI Research Paris & Inria Lille) · Olivier Teytaud (Facebook) · Alessandro Lazaric (Facebook Artificial Intelligence Research) · Matteo Pirotta (Facebook AI Research)

Generating Correct Answers for Progressive Matrices Intelligence Tests
Niv Pekar (Tel Aviv University) · Yaniv Benny (Tel Aviv University) · Lior Wolf (Facebook AI Research)

Fair Multiple Decision Making Through Soft Interventions
Yaowei Hu (University of Arkansas) · Yongkai Wu (Clemson University) · Lu Zhang (University of Arkansas) · Xintao Wu (University of Arkansas)

Neural Sparse Representation for Image Restoration
Yuchen Fan (University of Illinois at Urbana-Champaign) · Jiahui Yu (UIUC) · Yiqun Mei (University of Illinois) · Yulun Zhang (Northeastern University) · Yun Fu (Northeastern University) · Ding Liu (Bytedance AI Lab) · Thomas Huang (University of Illinois)

Regularized linear autoencoders recover the principal components, eventually
Xuchan Bao (University of Toronto) · James Lucas (University of Toronto) · Sushant Sachdeva (University of Toronto) · Roger Grosse (University of Toronto)

Robust, Accurate Stochastic Optimization for Variational Inference
Akash Kumar Dhaka (Aalto University) · Alejandro Catalina (Aalto University) · Michael Andersen (Aalto University) · Måns Magnusson (Aalto University) · Jonathan Huggins (Boston University) · Aki Vehtari (Aalto University)

Robustness of Bayesian Neural Networks to Gradient-Based Adversarial Attacks
Ginevra Carbone (University of Trieste) · Matthew Wicker (University of Oxford) · Luca Laurenti (University of Oxford) · Andrea Patane' (University of Oxford) · Luca Bortolussi (University of Trieste, Department of Mathematics and Geosciences) · Guido Sanguinetti (University of Edinburgh)

On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression
Denny Wu (University of Toronto & Vector Institute) · Ji Xu (Columbia University)

First Order Constrained Optimization in Policy Space
Yiming Zhang (New York University) · Quan Vuong (University of California, San Diego) · Keith Ross (NYU Shanghai)

Neural Manifold Ordinary Differential Equations
Aaron Lou (Cornell University) · Derek Lim (Cornell University) · Isay Katsman (Cornell University) · Leo Huang (Cornell University) · Qingxuan Jiang (Cornell University) · Ser Nam Lim (Facebook AI) · Christopher De Sa (Cornell)

Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View
Christos Thrampoulidis (UCSB) · Samet Oymak (University of California Berkeley) · Mahdi Soltanolkotabi (University of Southern california)

A Closer Look at Accuracy vs. Robustness
Yao-Yuan Yang (UCSD) · Cyrus Rashtchian (UCSD) · Hongyang Zhang (TTIC) · Russ Salakhutdinov (Carnegie Mellon University) · Kamalika Chaudhuri (UCSD)

Parametric Instance Classification for Unsupervised Visual Feature learning
Yue Cao (Microsoft Research) · Zhenda Xie (Tsinghua University) · Bin Liu (Tsinghua University) · Yutong Lin (Xi'an Jiaotong University) · Zheng Zhang (MSRA) · Han Hu (Microsoft Research Asia)

The Smoothed Possibility of Social Choice
Lirong Xia (RPI)

Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
Wenrui Zhang (University of California, Santa Barbara) · Peng Li (University of California, Santa Barbara)

Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks
Ryo Karakida (National Institute of Advanced Industrial Science and Technology) · Kazuki Osawa (Tokyo Institute of Technology)

Can the Brain Do Backpropagation?
Yuhang Song (University of Oxford) · Thomas Lukasiewicz (University of Oxford) · Zhenghua Xu (Hebei University of Technology) · Rafal Bogacz (University of Oxford)

Co-Tuning for Transfer Learning
Kaichao You (Tsinghua University) · Zhi Kou (Tsinghua University) · Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University)

A Study on Encodings for Neural Architecture Search
Colin White (RealityEngines.AI) · Willie Neiswanger (Carnegie Mellon University) · Sam Nolen (RealityEngines.AI) · Yash Savani (RealityEngines.AI)

Domain Adaptation as a Problem of Inference on Graphical Models
Kun Zhang (CMU) · Mingming Gong (University of Melbourne) · Petar Stojanov (Carnegie Mellon Univerisity) · Biwei Huang (Carnegie Mellon University) · QINGSONG LIU (Unisound Intelligence Co., Ltd.) · Clark Glymour (Carnegie Mellon University)

Cascaded Text Generation with Markov Transformers
Yuntian Deng (Harvard University) · Alexander Rush (Cornell University)

Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction
Michael Janner (UC Berkeley) · Igor Mordatch (Google) · Sergey Levine (UC Berkeley)

ARMA Nets: Expanding Receptive Field for Dense Prediction
Jiahao Su (University of Maryland) · Shiqi Wang (Nanjing University ) · Furong Huang (University of Maryland)

Stochastic Deep Gaussian Processes over Graphs
Naiqi Li (Tsinghua-Berkeley Shenzhen Institute) · Wenjie Li (Tsinghua University) · Jifeng Sun (Tsinghua University) · Yinghua Gao (Tsinghua University) · Yong Jiang (Tsinghua) · Shu-Tao Xia (Tsinghua University)

Rankmax: An Adaptive Projection Alternative to the Softmax Function
Weiwei Kong (Georgia Institute of Technology) · Walid Krichene (Google) · Nicolas E Mayoraz (Google, Inc.) · Steffen Rendle (Google) · Li Zhang (Google)

Scalable Approximation Algorithm for Fair $k-$center Clustering
Elfarouk Harb (Independent Researcher) · Ho Shan Lam (The Hong Kong University of Science and Technology)

Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
Yongqing Liang (Louisiana State University) · Xin Li (Louisiana State University) · Navid Jafari (Louisiana State University) · Jim Chen (Northeastern University)

An Efficient Adversarial Attack for Tree Ensembles
Chong Zhang (UCLA) · Huan Zhang (UCLA) · Cho-Jui Hsieh (UCLA)

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning
Zhongzheng Ren (UIUC) · Raymond Yeh (University of Illinois at Urbana–Champaign) · Alexander Schwing (University of Illinois at Urbana-Champaign)

MPNet: Masked and Permuted Pre-training for Language Understanding
Kaitao Song (Nanjing University of Science and technology) · Xu Tan (Microsoft Research) · Tao Qin (Microsoft Research) · Jianfeng Lu (Nanjing University of Science and Technology) · Tie-Yan Liu (Microsoft Research Asia)

Fighting Copycat Agents in Behavioral Cloning from Multiple Observations
Chuan Wen (Tsinghua University) · Jierui Lin (University of California, Berkeley) · Trevor Darrell (UC Berkeley) · Dinesh Jayaraman (University of Pennsylvania) · Yang Gao (UC Berkeley)

Self-Distillation Amplifies Regularization in Hilbert Space
Hossein Mobahi (Google Research) · Mehrdad Farajtabar (DeepMind) · Peter Bartlett (UC Berkeley)

GreedyFool: Distortion-Aware Sparse Adversarial Attack
Xiaoyi Dong (University of Science and Technology of China) · Dongdong Chen (Microsoft Cloud AI) · Jianmin Bao (Microsoft Research) · Chuan Qin (University of Science and Technology of China) · Lu Yuan (Microsoft) · Weiming Zhang (University of Science and Technology of China) · Nenghai Yu (University of Science and Technology of China) · Dong Chen (Microsoft Research Asia)

Minimax classification with 0-1 loss and performance guarantees
Santiago Mazuelas (Basque Center for Applied Mathematics) · Andrea Zanoni (Ecole Polytechnique Federale de Lausanne) · Aritz Pérez (Basque Center for Applied Mathematics (BCAM))

Gradient Regularized V-Learning for Dynamic Treatment Regimes
Yao Zhang (University of Cambridge) · Mihaela van der Schaar (University of Cambridge)

Learning outside the Black-Box: The pursuit of interpretable models
Jonathan Crabbe (University of Cambridge) · Yao Zhang (University of Cambridge) · William Zame (UCLA) · Mihaela van der Schaar (University of Cambridge)

Deep Multimodal Fusion by Channel Exchanging
Yikai Wang (Tsinghua University) · Wenbing Huang (Tsinghua University) · Fuchun Sun (Tsinghua) · Tingyang Xu (Tencent AI Lab) · Yu Rong (Tencent AI Lab) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
Yonggan Fu (Rice University) · Haoran You (Rice University) · Yang Zhao (Rice University) · Yue Wang (Rice University) · Chaojian Li (Rice University) · Kailash Gopalakrishnan (IBM Research) · Zhangyang Wang (University of Texas at Austin) · Yingyan Lin (Rice University)

Faster Wasserstein Distance Estimation with the Sinkhorn Divergence
Lénaïc Chizat (CNRS) · Pierre Roussillon (ENS) · Flavien Léger (ENS) · François-Xavier Vialard (University Gustave Eiffel) · Gabriel Peyré (CNRS and ENS)

Implicit Regularization in Deep Learning May Not Be Explainable by Norms
Noam Razin (Tel Aviv University) · Nadav Cohen (Tel Aviv University)

Scalable Graph Neural Networks via Bidirectional Propagation
Ming Chen (Renmin University of China) · Zhewei Wei (Renmin University of China) · Bolin Ding ("Data Analytics and Intelligence Lab, Alibaba Group") · Yaliang Li (Alibaba Group) · Ye Yuan ( Beijing Institute of Technology) · Xiaoyong Du (Renmin University of China) · Ji-Rong Wen (Renmin University of China)

Online Matrix Completion with Side Information
Mark Herbster (University College London) · Stephen Pasteris (University College London) · Fai Yu Lisa Tse (University College London)

No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
Nimit S Sohoni (Stanford University) · Jared Dunnmon (Stanford University) · Geoffrey Angus (Stanford University) · Albert Gu (Stanford) · Christopher Ré (Stanford)

Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst Case Rates
Kaiwen Zhou (The Chinese University of Hong Kong) · Anthony Man-Cho So (CUHK) · James Cheng (The Chinese University of Hong Kong)

Deep Energy-based Modeling of Discrete-Time Physics
Takashi Matsubara (Osaka University) · Ai Ishikawa (Kobe University) · Takaharu Yaguchi (Kobe University)

UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging
Chu Zhou (Peking University) · Hang Zhao (MIT) · Jin Han (Peking University) · Chang Xu (University of Sydney) · Chao Xu (Peking University) · Tiejun Huang (Peking University) · Boxin Shi (Peking University)

Learning to search efficiently for causally near-optimal treatments
Samuel Håkansson (Chalmers University of Technology) · Viktor Lindblom (Chalmers University of Technology) · Omer Gottesman (Harvard University) · Fredrik Johansson (Chalmers University of Technology)

Supervised Contrastive Learning
Prannay Khosla (Google LLC) · Piotr Teterwak (Google) · Chen Wang (Google) · Aaron Sarna (Google) · Yonglong Tian (MIT) · Phillip Isola (Massachusetts Institute of Technology) · Aaron Maschinot (Google Research) · Ce Liu (Google) · Dilip Krishnan (Google)

A mean-field analysis of two-player zero-sum games
Carles Domingo-Enrich (NYU) · Samy Jelassi (Princeton University) · Arthur Mensch (ENS) · Grant Rotskoff (New York University) · Joan Bruna (NYU)

GROVER: Self-Supervised Message Passing Transformer on Large-scale Molecular Graphs
Yu Rong (Tencent AI Lab) · Yatao Bian (Tencent AI Lab) · Tingyang Xu (Tencent AI Lab) · Weiyang Xie (Tencent AI Lab) · Ying WEI (Tencent AI Lab) · Wenbing Huang (Tsinghua University) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN
Tao Fang (Zhejiang University) · Yu Qi (Zhejiang University) · Gang Pan (Zhejiang University)

Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
Zhanqiu Zhang (University of Science and Technology of China) · Jianyu Cai (University of Science and Technology of China) · Jie Wang (University of Science and Technology of China)

Bayesian Pseudocoresets
Dionysis Manousakas (University of Cambridge) · Zuheng Xu (University of British Columbia) · Cecilia Mascolo (University of Cambridge) · Trevor Campbell (UBC)

Set2Graph: Learning Graphs From Sets
Hadar Serviansky (Weizmann Institute of Science) · Nimrod Segol (Weizmann Institute of Science) · Jonathan Shlomi (Weizmann Institute of Science) · Kyle Cranmer (New York University) · Eilam Gross (Weizmann Institute of Science) · Haggai Maron (NVIDIA Research) · Yaron Lipman (Weizmann Institute of Science)

A Closer Look at the Training Strategy for Modern Meta-Learning
JIAXIN CHEN (The Hong Kong Polytechnic University) · Xiao-Ming Wu (The Hong Kong Polytechnic University) · Yanke Li (ETH Zurich) · Qimai LI (The Hong Kong PolyU) · Li-Ming Zhan (The Hong Kong Polytechnic University) · Fu-lai Chung (The Hong Kong Polytechnic University)

Hard Shape-Constrained Kernel Machines
Pierre-Cyril Aubin-Frankowski (MINES ParisTech) · Zoltan Szabo (Ecole Polytechnique)

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning
Julien Roy (Mila) · Paul Barde (Quebec AI institute - Mila, McGill) · Félix G Harvey (Polytechnique Montréal) · Derek Nowrouzezahrai (McGill University) · Chris Pal (MILA, Polytechnique Montréal, Element AI)

Bandit Linear Control
Asaf Cassel (Tel Aviv University) · Tomer Koren (Tel Aviv University & Google)

Adapting Neural Architectures Between Domains
Yanxi Li (University of Sydney) · zhaohui yang (peking university) · Yunhe Wang (Huawei Noah's Ark Lab) · Chang Xu (University of Sydney)

Introducing Routing Uncertainty in Capsule Networks
Fabio De Sousa Ribeiro (University of Lincoln) · Georgios Leontidis (University of Aberdeen) · Stefanos Kollias (University of Lincoln)

Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth
Guy Bresler (MIT) · Dheeraj Nagaraj (Massachusetts Institute of Technology)

On the Similarity between the Laplace and Neural Tangent Kernels
Amnon Geifman (Weizmann Institute) · Abhay Yadav (University of Maryland) · Yoni Kasten (Weizmann Institute) · Meirav Galun (Weizmann Institute of Science) · David Jacobs (University of Maryland) · Basri Ronen (Weizmann Inst.)

Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
Chung-Wei Lee (University of Southern California) · Haipeng Luo (University of Southern California) · Chen-Yu Wei (University of Southern California) · Mengxiao Zhang (University of Southern California)

Generalized Video Frame Interpolation
Youjian Zhang (the University of Sydney) · Chaoyue Wang (University of Sydney) · Dacheng Tao (University of Sydney)

Hybrid Variance-Reduced SGD Algorithms for Nonconvex-Concave Minimax Problems
Quoc Tran Dinh (Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, North Carolina) · Deyi Liu (University of North Carolina) · Lam Nguyen (IBM Research, Thomas J. Watson Research Center)

First-order methods for large-scale market equilibrium computation
Yuan Gao (Columbia University) · Christian Kroer (Columbia University)

Matern Gaussian processes on Riemannian manifolds
Viacheslav Borovitskiy (St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences (PDMI RAS)) · Alexander Terenin (Petuum, Inc. and Imperial College London) · Peter Mostowsky (St. Petersburg State University) · Marc Deisenroth (University College London)

Minimax Value Interval for Off-Policy Evaluation and Policy Optimization
Nan Jiang (University of Illinois at Urbana-Champaign) · Jiawei Huang (University of Illinois at Urbana-Champaign)

Neural Controlled Differential Equations for Irregular Time Series
Patrick Kidger (University of Oxford) · James Morrill (University of Oxford) · James Foster (University of Oxford) · Terry Lyons (University of Oxford)

AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference
Qian Lou (Indiana University Bloomington) · Song Bian (Kyoto University) · Lei Jiang (Indiana University Bloomington)

Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning
Nathan Kallus (Cornell University) · Angela Zhou (Cornell University)

A kernel test for quasi-independence
Tamara Fernandez (University College London) · Wenkai Xu (Gatsby Unit, UCL) · Marc Ditzhaus (TU Dortmund University) · Arthur Gretton (Gatsby Unit, UCL)

BOSS: Bayesian Optimization over String Spaces
Henry Moss (Lancaster University) · David Leslie (Lancaster University and PROWLER.io) · Daniel Beck (University of Melbourne) · Javier Gonzalez (Amazon.com) · Paul Rayson (Lancaster University)

Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
Tiancheng Jin (University of Southern California) · Haipeng Luo (University of Southern California)

Audeo: Audio Generation for a Silent Performance Video
Kun Su (University of Washington Seattle) · Xiulong Liu (University of Washington) · Eli Shlizerman (Departments of Applied Mathematics and Electrical & Computer Engineering, University of Washington Seattle)

Modular Bayesian Optimization with BoTorch: An Efficient Differentiable Monte-Carlo Approach
Maximilian Balandat (Facebook) · Brian Karrer (Facebook) · Daniel Jiang (Facebook) · Samuel Daulton (Facebook) · Ben Letham (Facebook) · Andrew Gordon Wilson (New York University) · Eytan Bakshy (Facebook)

Adversarially Robust Streaming Algorithms via Differential Privacy
Avinatan Hasidim (Google) · Haim Kaplan (TAU, GOOGLE) · Yishay Mansour (Tel Aviv University / Google) · Yossi Matias (Google) · Uri Stemmer (Ben-Gurion University)

Certified Monotonic Neural Networks
Xingchao Liu (University of Texas at Austin) · Xing Han (The University of Texas at Austin) · Na Zhang (Tsinghua University) · Qiang Liu (UT Austin)

Integrating Multi-domain Outcomes for Learning Optimal Individualized Treatment Rules
Yuan Chen (Columbia University) · Donglin Zeng (University of North Carolina at Chapel Hill) · Tianchen Xu (Columbia University) · Yuanjia Wang (Columbia University)

Multiscale Deep Equilibrium Models
Shaojie Bai (Carnegie Mellon University) · Vladlen Koltun (Intel Labs) · J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

ShapeFlow: Learnable Deformation Flows Among 3D Shapes
Chiyu Jiang (UC Berkeley) · Jingwei Huang (Stanford University) · Andrea Tagliasacchi (Google Research, Brain) · Leonidas J Guibas (stanford.edu)

BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images
Thu Nguyen-Phuoc (University of Bath) · Christian Richardt (University of Bath) · Long Mai (Adobe Research) · Yongliang Yang (University of Bath) · Niloy Mitra (University College London)

Faster DBSCAN via subsampled similarity queries
Heinrich Jiang (Google Research) · Jennifer Jang (Uber) · Jakub Lacki (Google)

Wavelet Flow: Fast Training of High Resolution Normalizing Flows
Jason Yu (York University) · Konstantinos Derpanis (Ryerson University) · Marcus Brubaker (York University)

Learning Retrospective Knowledge with Reverse Reinforcement Learning
Shangtong Zhang (University of Oxford) · Vivek Veeriah (University of Michigan) · Shimon Whiteson (University of Oxford)

Probabilistic Active Meta-Learning
Jean Kaddour (University College London) · Steindor Saemundsson (Imperial College London) · Marc Deisenroth (University College London)

Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider (University of Tübingen) · Evgenia Rusak (University of Tuebingen) · Luisa Eck (LMU Munich) · Oliver Bringmann (University of Tübingen) · Wieland Brendel (AG Bethge, University of Tübingen) · Matthias Bethge (University of Tübingen)

A Decentralized Parallel Algorithm for Training Generative Adversarial Nets
Mingrui Liu (Boston University) · Wei Zhang (IBM T.J.Watson Research Center) · Youssef Mroueh (IBM T.J Watson Research Center) · Xiaodong Cui (IBM T. J. Watson Research Center) · Jarret Ross (IBM) · Tianbao Yang (The University of Iowa) · Payel Das (IBM Research)

Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
Yuxuan Zhao (Cornell University) · Madeleine Udell (Cornell University)

Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
Noam Brown (Facebook AI Research) · Anton Bakhtin (Facebook AI Research) · Adam Lerer (Facebook AI Research) · Qucheng Gong (Facebook AI Research)

Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield
John Choi (New York University) · Krishan Kumar (New York University) · Mohammad Khazali (New York University) · Katie Wingel (New York University) · Mahdi Choudhury (New York University) · Adam Charles (Johns Hopkins University) · Bijan Pesaran (New York University)

High-Dimensional Sparse Linear Bandits
Botao Hao (Deepmind) · Tor Lattimore (DeepMind) · Mengdi Wang (Princeton University)

Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation
Yogesh Balaji (University of Maryland) · Rama Chellappa (University of Maryland College Park) · Soheil Feizi (University of Maryland)

Trade-offs and Guarantees on Adversarial Representation Learning for Information Obfuscation
Han Zhao (Carnegie Mellon University) · Jianfeng Chi (University of Virginia) · Yuan Tian (University of Virginia) · Geoffrey Gordon (MSR Montréal & CMU)

Bayesian Optimization of Risk Measures
Sait Cakmak (Georgia Institute of Technology) · Raul Astudillo Marban (Cornell University) · Peter Frazier (Cornell / Uber) · Enlu Zhou (Georgia Institute of Technology)

Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Hadi Salman (Microsoft Research AI) · Mingjie Sun (Carnegie Mellon University) · Greg Yang (Microsoft Research) · Ashish Kapoor (Microsoft) · J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

Batched Coarse Ranking in Multi-Armed Bandits
Nikolai Karpov (Indiana University Bloomington) · Qin Zhang (Indiana University Bloomington)

Bandit-PAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits
Mo Tiwari (Stanford University) · Martin Zhang (Harvard University) · James J Mayclin (Stanford University) · Sebastian Thrun (Stanford University) · Chris Piech (Stanford) · Ilan Shomorony (University of Illinois at Urbana Champaign)

Fast Unbalanced Optimal Transport on Tree
Ryoma Sato (Kyoto University) · Makoto Yamada (Kyoto University/RIKEN AIP) · Hisashi Kashima (Kyoto University/RIKEN Center for AIP)

Contrastive Learning with Adversarial Examples
Chih-Hui Ho (University of California San Diego) · Nuno Nvasconcelos (UC San Diego)

Weakly Supervised Deep Functional Maps for Shape Matching
Abhishek Sharma (Ecole Polytechnique) · Maks Ovsjanikov (Ecole polytechnique)

Baxter Permutation Process
Masahiro Nakano (NTT communication science laboratories) · Akisato Kimura (NTT Communication Science Laboratories) · Takeshi Yamada (NTT Communication Science Labs.) · Naonori Ueda (NTT Communication Science Labs. / RIKEN AIP)

Counterfactual Predictions under Runtime Confounding
Amanda Coston (Carnegie Mellon University) · Edward Kennedy (Carnegie Mellon University) · Alexandra Chouldechova (CMU)

Few-Cost Salient Object Detection with Adversarial-Paced Learning
Dingwen Zhang (Xidian University) · HaiBin Tian (Xidian University) · Jungong Han (University of Warwick)

Multipole Graph Neural Operator for Parametric Partial Differential Equations
Zongyi Li (Caltech) · Nikola Kovachki (California Institute of Technology) · Kamyar Azizzadenesheli (Caltech) · Burigede Liu (caltech) · Andrew Stuart (California Institute of Technology) · Kaushik Bhattacharya (Caltech) · Anima Anandkumar (NVIDIA / Caltech)

Generalized Boosting
Arun Suggala (Carnegie Mellon University) · Bingbin Liu () · Pradeep Ravikumar (Carnegie Mellon University)

Parameterized Explainer for Graph Neural Network
Dongsheng Luo (The Pennsylvania State University) · Wei Cheng (NEC Labs America) · Dongkuan Xu (The Pennsylvania State University) · Wenchao Yu (UCLA) · Bo Zong (NEC Labs) · Haifeng Chen (NEC Labs America) · Xiang Zhang (The Pennsylvania State University)

All Word Embeddings from One Embedding
Sho Takase (Tokyo Institute of Technology) · Sosuke Kobayashi (Preferred Networks)

Locally Differentially Private (Contextual) Bandits Learning
Kai Zheng (Kuaishou) · Tianle Cai (Peking University) · Weiran Huang (Noah's Ark Lab) · Zhenguo Li (Noah's Ark Lab, Huawei Tech Investment Co Ltd) · Liwei Wang (Peking University)

Multi-Label Classification: Does Hamming Loss and Subset Accuracy really conflict with each other?
Guoqiang Wu (Tsinghua University) · Jun Zhu (Tsinghua University)

Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry
Yossi Arjevani (NYU) · Michael Field (UC Santa Barbara)

Reinforced Molecular Optimization with Neighborhood-Controlled Grammars
Chencheng Xu (Tsinghua University) · Qiao Liu (Tsinghua University) · Minlie Huang (Tsinghua University) · Tao Jiang (University of California - Riverside)

Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
Minhae Kwon (Rice University, Baylor College of Medicine) · Saurabh Daptardar (Google) · Paul R Schrater (University of Minnesota) · Zachary Pitkow (BCM/Rice)

Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement
Miao Zhang (UTS&BIT) · Huiqi Li (Beijing Institute of Technology) · Shirui Pan (Monash University) · Xiaojun Chang (Monash University) · Zongyuan Ge (Monash University) · Steven Su (University of Technology Sydney)

Variance reduction for Langevin Monte Carlo in high dimensional sampling problems
ZHIYAN DING (University of Wisconsin-Madison) · Qin Li (University of Wisconsin-Madison)

Online Structured Meta-learning
Huaxiu Yao (Pennsylvania State University) · Yingbo Zhou (Salesforce Research) · Mehrdad Mahdavi (Pennsylvania State University) · Zhenhui (Jessie) Li (Penn State University) · Richard Socher (Salesforce) · Caiming Xiong (Salesforce)

Self-Supervised Visual Representation Learning from Hierarchical Grouping
Xiao Zhang (University of Chicago) · Michael Maire (University of Chicago)

MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Wenhui Wang (MSRA) · Furu Wei (Microsoft Research Asia) · Li Dong (Microsoft Research) · Hangbo Bao (Harbin Institute of Technology) · Nan Yang (Microsoft Research Asia) · Ming Zhou (Microsoft Research)

Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
Samuel Daulton (Facebook) · Maximilian Balandat (Facebook) · Eytan Bakshy (Facebook)

Energy-based Out-of-distribution Detection
Weitang Liu (UC San Diego) · Xiaoyun Wang (University of California, Davis) · John Owens (University of California, Davis) · Sharon Yixuan Li (Stanford University)

Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
Hung Tran-The (Deakin University) · Sunil Gupta (Deakin University) · Santu Rana (Deakin University) · Huong Ha (Deakin University) · Svetha Venkatesh (Deakin University)

Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
Kaihua Tang (Nanyang Technological University) · Jianqiang Huang (Damo Academy, Alibaba Group) · Hanwang Zhang (NTU)

CircleGAN: Generative Adversarial Learning across Spherical Circles
Woohyeon Shim (Postech) · Minsu Cho (POSTECH)

Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
Yaodong Yu (University of California, Berkeley) · Kwan Ho Ryan Chan (University of California, Berkeley) · Chong You (University of California, Berkeley) · Chaobing Song (Tsinghua University) · Yi Ma (UC Berkeley)

Noise-Contrastive Estimation for Multivariate Point Processes
Hongyuan Mei (JOHNS HOPKINS UNIVERSITY) · Tom Wan (JOHNS HOPKINS UNIVERSITY) · Jason Eisner (Johns Hopkins + Microsoft)

POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
Yeong-Dae Kwon (Samsung SDS) · Jinho Choo (Samsung SDS) · Byoungjip Kim (Samsung SDS) · Iljoo Yoon (Samsung SDS) · Youngjune Gwon (Samsung SDS) · Seungjai Min (Samsung SDS)

Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables
Guangyao Zhou (Vicarious AI)

Data Diversification: A Simple Strategy For Neural Machine Translation
Xuan-Phi Nguyen (Nanyang Technological University) · Shafiq Joty (Nanyang Technological University) · Kui Wu (Institute for Infocomm Research, Singapore) · Ai Ti Aw (Institute for Infocomm Research)

AutoBSS: An Efficient Algorithm for Block Stacking Style Search
yikang zhang (Huawei Digital Technologies Co., Ltd.) · Jian Zhang (Huawei Technologies Co., Ltd.) · Zhao Zhong (HUAWEI)

Escaping Saddle-Point Faster under Interpolation-like Conditions
Abhishek Roy (University of California, Davis) · Krishnakumar Balasubramanian (University of California, Davis) · Saeed Ghadimi (Princeton University) · Prasant Mohapatra (University of California, Davis)

Neural Networks Fail to Learn Periodic Functions and How to Fix It
Ziyin Liu (University of Tokyo) · Tilman Hartwig (University of Tokyo) · Masahito Ueda (University of Tokyo)

Self-Supervised Relational Reasoning for Representation Learning
Massimiliano Patacchiola (University of Edinburgh) · Amos Storkey (University of Edinburgh)

Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
Joel Dapello (Harvard University) · Tiago Marques (MIT) · Martin Schrimpf (MIT) · Franziska Geiger (MIT) · David Cox (MIT-IBM Watson AI Lab) · James J DiCarlo (Massachusetts Institute of Technology)

Finite Contiuum-Armed Bandits
Solenne Gaucher (Université Paris-Saclay)

Multi-task Causal Learning with Gaussian Processes
Virginia Aglietti (University of Warwick) · Theodoros Damoulas (University of Warwick & The Alan Turing Institute) · Mauricio Álvarez (University of Sheffield) · Javier Gonzalez (Amazon.com)

Random Walk Graph Neural Networks
Giannis Nikolentzos (Athens University of Economics and Business) · Michalis Vazirgiannis (École Polytechnique)

Training Generative Adversarial Networks with Limited Data
Tero Karras (NVIDIA) · Miika Aittala (MIT CSAIL / NVIDIA) · Janne Hellsten (NVIDIA) · Samuli Laine (NVIDIA) · Jaakko Lehtinen (Aalto University & NVIDIA) · Timo Aila (NVIDIA)

Entropic Optimal Transport between (Unbalanced) Gaussian Measures has a Closed Form
Hicham Janati (Inria / ENSAE) · Boris Muzellec (ENSAE, Institut Polytechnique de Paris) · Gabriel Peyré (CNRS and ENS) · Marco Cuturi (Google Brain & CREST - ENSAE)

Self-Paced Deep Reinforcement Learning
Pascal Klink (TU Darmstadt) · Carlo D'Eramo (TU Darmstadt) · Jan Peters (TU Darmstadt & MPI Intelligent Systems) · Joni Pajarinen (TU Darmstadt)

An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search
Kyunghyun Lee (KAIST) · Byeong-Uk Lee (KAIST) · Ukcheol Shin (KAIST) · In So Kweon (KAIST)

Online Influence Maximization under Linear Threshold Model
Shuai Li (Shanghai Jiao Tong University) · Fang Kong (Shanghai Jiao Tong University) · Kejie Tang (Shanghai Jiao Tong University) · Qizhi Li (Shanghai Jiao Tong University) · Wei Chen (Microsoft Research)

Federated Bayesian Optimization via Thompson Sampling
Zhongxiang Dai (National University of Singapore) · Bryan Kian Hsiang Low (National University of Singapore) · Patrick Jaillet (MIT)

Inverse Learning of Symmetries
Mario Wieser (University of Basel) · Sonali Parbhoo (Harvard University) · Aleksander Wieczorek (University of Basel) · Volker Roth (University of Basel)

MESA: Effective Ensemble Imbalanced Learning with MEta-SAmpler
Zhining Liu (Jilin University) · Pengfei Wei (National University of Singapore) · Jing Jiang (University of Technology Sydney) · Wei Cao (MSRA) · Jiang Bian (Microsoft) · Yi Chang (Jilin University)

ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks
Shuxuan Guo (EPFL) · Jose M. Alvarez (NVIDIA) · Mathieu Salzmann (EPFL)

Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
Shunming Li (Alibaba Group) · Huan Fu (Alibaba Group) · Rongfei Jia (Alibaba Group) · Mingming Gong (University of Melbourne) · Binqiang Zhao (Alibaba Corp) · Dacheng Tao (University of Sydney)

High-Fidelity Generative Image Compression
Fabian Mentzer (ETH Zurich) · George D Toderici (Google) · Michael Tschannen (Google Brain) · Eirikur Agustsson (Google)

Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
Abhinav Agrawal (UMass Amherst) · Daniel Sheldon (University of Massachusetts Amherst) · Justin Domke (University of Massachusetts, Amherst)

Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
Jakob Lindinger (Bosch Center for Artificial Intelligence) · David Reeb (Bosch Center for Artificial Intelligence (BCAI)) · Christoph Lippert (Hasso Plattner Institute for Digital Engineering, Universität Potsdam) · Barbara Rakitsch (Bosch Center for Artificial Intelligence)

Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions
Hayata Yamasaki (The University of Tokyo) · Sathyawageeswar Subramanian (University of Cambridge) · Sho Sonoda (RIKEN AIP) · Masato Koashi (The University of Tokyo)

Domain Generalization via Entropy Regularization
Shanshan Zhao (The University of Sydney) · Mingming Gong (University of Melbourne) · Tongliang Liu (The University of Sydney) · Huan Fu (Alibaba Group) · Dacheng Tao (University of Sydney)

Impossibility Results for Grammar-Compressed Linear Algebra
Amir Abboud (IBM research) · Arturs Backurs (TTIC) · Karl Bringmann (Saarland University) · Marvin Künnemann (Max-Planck-Institut für Informatik)

Parabolic Approximation Line Search for DNNs
Maximus Mutschler (University of Tübingen) · Andreas Zell (University of Tuebingen)

Revisiting Parameter Sharing for Automatic Neural Channel Number Search
Jiaxing Wang (Institute of Automation, Chinese Academy of Sciences) · Haoli Bai (The Chinese University of Hong Kong) · Jiaxiang Wu (Tencent AI Lab) · Xupeng Shi (Northeastern University) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab) · Irwin King (Chinese University of Hong Kong) · Michael Lyu (CUHK) · Jian Cheng (Institute of Automation, Chinese Academy of Sciences)

Lipschitz-Certifiable Training with a Tight Outer Bound
Sungyoon Lee (Seoul National University) · Jaewook Lee (Seoul National University) · Saerom Park (Sungshin Women's University)

Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms
Thomas Berrett (CREST, ENSAE, Institut Polytechnique de Paris) · Cristina Butucea (CREST, ENSAE, Institut Polytechnique de Paris)

Improved Algorithms for Convex-Concave Minimax Optimization
Yuanhao Wang (Tsinghua University) · Jian Li (Tsinghua University)

Neural Star Domain as Primitive Representation
Yuki Kawana (The University of Tokyo) · Yusuke Mukuta (The University of Tokyo) · Tatsuya Harada (The University of Tokyo / RIKEN)

GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
Nasir Ahmad (Donders Institute for Brain, Cognition and Behaviour, Radboud University) · Marcel A. J. van Gerven (Radboud Universiteit) · Luca Ambrogioni (Radboud University)

Dirichlet Graph Variational Autoencoder
Jia Li (The Chinese University of Hong Kong) · Jianwei Yu (CUHK) · Jiajin Li (The Chinese University of Hong Kong) · Honglei Zhang (Georgia Institute of Technology) · Kangfei Zhao (The Chinese University of Hong Kong) · Yu Rong (Tencent AI Lab) · Hong Cheng (The Chinese University of Hong Kong) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

Adaptive Sampling for Stochastic Risk-Averse Learning
Sebastian Curi (ETH Zürich) · Kfir Y. Levy (Technion) · Stefanie Jegelka (MIT) · Andreas Krause (ETH Zurich)

Deep Structural Causal Models for Tractable Counterfactual Inference
Nick Pawlowski (Imperial College London) · Daniel Coelho de Castro (Imperial College London) · Ben Glocker (Imperial College London)

Rethinking Importance Weighting for Deep Learning under Distribution Shift
Tongtong Fang (The University of Tokyo) · Nan Lu (University of Tokyo/ RIKEN-AIP) · Gang Niu (RIKEN) · Masashi Sugiyama (RIKEN / University of Tokyo)

Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
Sebastian Curi (ETH Zürich) · Felix Berkenkamp (ETH Zurich) · Andreas Krause (ETH Zurich)

Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies
Nathan Kallus (Cornell University) · Masatoshi Uehara (Cornell University)

A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model
Steffen Czolbe (University of Copenhagen) · Oswin Krause (University of Copenhagen) · Ingemar Cox (University College London) · Christian Igel (University of Copenhagen)

Off-Policy Evaluation and Learning for External Validity under a Covariate Shift
Masatoshi Uehara (Cornell University) · Masahiro Kato (The University of Tokyo) · Shota Yasui (Cyberagent)

DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs
yaxing wang (Centre de Visió per Computador (CVC)) · Lu Yu (computer vision center, UAB) · Joost van de Weijer (Computer Vision Center Barcelona)

Natural Graph Networks
Pim de Haan (Qualcomm AI Research, University of Amsterdam) · Taco Cohen (Qualcomm AI Research) · Max Welling (University of Amsterdam / Qualcomm AI Research)

Adversarial Sparse Transformer for Time Series Forecasting
Sifan Wu (Tsinghua University) · Xi Xiao (Tsinghua University) · Qianggang Ding (Tsinghua University) · Peilin Zhao (Tencent AI Lab) · Ying Wei (Tencent AI Lab) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
Kenta Oono (The University of Tokyo, Preferred Networks Inc.) · Taiji Suzuki (The University of Tokyo/RIKEN-AIP)

Unsupervised object-centric video generation and decomposition in 3D
Paul Henderson (IST Austria) · Christoph Lampert (IST Austria)

Causal Intervention for Weakly-Supervised Semantic Segmentation
Dong Zhang (Nanjing University of Science and Technology) · Hanwang Zhang (NTU) · Jinhui Tang (Nanjing University of Science and Technology) · Xian-Sheng Hua (Damo Academy, Alibaba Group) · Qianru Sun (Singapore Management University)

Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
Shir Gur (Tel Aviv University) · Sagie Benaim (Tel Aviv University) · Lior Wolf (Facebook AI Research)

Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms
Tengyu Xu (The Ohio State University) · Zhe Wang (Ohio State University) · Yingbin Liang (The Ohio State University)

Neuron-level Structured Pruning using Polarization Regularizer
Tao Zhuang (Alibaba Group) · Zhixuan Zhang (Beijing University of Posts and Telecommunications) · Yuheng Huang (Beijing Univ. of Posts and Telecommunications) · Xiaoyi Zeng (Alibaba Group) · Kai Shuang (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.) · Xiang Li (Alibaba Group)

Further Analysis of Outlier Detection with Deep Generative Models
Ziyu Wang (Tsinghua University) · Bin Dai (Samsung Research China - Beijing) · David P Wipf (Microsoft Research Asia) · Jun Zhu (Tsinghua University)

TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation
Chun-Hsing Lin (National Taiwan University) · Siang-Ruei Wu (National Taiwan University) · Hung-yi Lee (National Taiwan University) · Yun-Nung Chen (National Taiwan University)

Bayesian Deep Learning and a Probabilistic Perspective of Generalization
Andrew Gordon Wilson (New York University) · Pavel Izmailov (New York University)

Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine
Jiajin Li (The Chinese University of Hong Kong) · Caihua Chen (Nanjing University) · Anthony Man-Cho So (CUHK)

Exact Recovery of Mangled Clusters with Same-Cluster Queries
Marco Bressan (Sapienza University of Rome) · Nicolò Cesa-Bianchi (Università degli Studi di Milano) · Silvio Lattanzi (Google Research) · Andrea Paudice (University of Milan)

Woodbury Transformations for Deep Generative Flows
You Lu (Virginia Tech) · Bert Huang (Virginia Tech)

Self-Supervised MultiModal Versatile Networks
Jean-Baptiste Alayrac (Deepmind) · Adria Recasens (DeepMind) · Rosalia Schneider (DeepMind) · Relja Arandjelović (DeepMind) · Jason Ramapuram (University of Geneva) · Jeffrey De Fauw (DeepMind) · Lucas Smaira (DeepMind) · Sander Dieleman (DeepMind) · Andrew Zisserman (DeepMind & University of Oxford)

Skeleton-bridged Point Completion: From Global Inference to Local Adjustment
Yinyu Nie (Bournemouth University) · Yiqun Lin (The Chinese University of Hong Kong, Shenzhen) · Xiaoguang Han (Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong (Shenzhen)) · Shihui Guo (Xiamen University) · Jian Chang (Bournemouth University) · Shuguang Cui (The Chinese University of Hong Kong, Shenzhen) · Jian.J Zhang (Bournemouth University)

Untangling tradeoffs between recurrence and self-attention in artificial neural networks
Giancarlo Kerg (MILA) · Bhargav Kanuparthi (Montreal Institute for Learning Algorithms) · Anirudh Goyal ALIAS PARTH GOYAL (Université de Montréal) · Kyle Goyette (University of Montreal) · Yoshua Bengio (Mila / U. Montreal) · Guillaume Lajoie (Mila, Université de Montréal)

A Bayesian Nonparametrics View into Deep Representations
Michał Jamroż (AGH University of Science and Technology) · Marcin Kurdziel (AGH University of Science and Technology, Krakow, Poland) · Mateusz Opala (AGH University of Science and Technology)

Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching
Di Hu (Renmin University of China) · Rui Qian (Shanghai Jiao Tong University) · Minyue Jiang (Baidu Inc.) · Xiao Tan (Baidu Inc.) · Shilei Wen (BAIDU) · Errui Ding (Baidu Inc.) · Weiyao Lin (Shanghai Jiao Tong university) · Dejing Dou (Baidu)

Dynamic Regret of Convex and Smooth Functions
Peng Zhao (Nanjing University) · Yu-Jie Zhang (Nanjing University) · Lijun Zhang (Nanjing University (NJU)) · Zhi-Hua Zhou (Nanjing University)

Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity
Dan Garber (Technion - Israel Institute of Technology)

Unbalanced Sobolev Descent
Youssef Mroueh (IBM T.J Watson Research Center) · Mattia Rigotti (IBM Research AI)

Information Maximization for Few-Shot Learning
Malik Boudiaf (Ecole de Technologie Superieure) · Imtiaz Ziko (Ecole de technologie superieure (ETS)) · Jérôme Rony (ÉTS Montréal) · Jose Dolz (ETS Montreal) · Pablo Piantanida (CentraleSupélec - Mila) · Ismail Ben Ayed (ETS Montreal)

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
Ignavier Ng (University of Toronto) · AmirEmad Ghassami (Johns Hopkins University) · Kun Zhang (CMU)

Towards Neural Program Interfaces
Zachary Brown (Duke University) · Nathaniel Robinson (Brigham Young University) · David Wingate (Brigham Young University) · Nancy Fulda (Brigham Young University)

Bridging Visual Representations for Object Detection
Cheng Chi (University of Chinese Academy of Sciences) · Fangyun Wei (Microsoft Research Asia) · Han Hu (Microsoft Research Asia)

Bayesian Optimization for Iterative Learning
Vu Nguyen (University of Oxford) · Sebastian Schulze (University of Oxford) · Michael A Osborne (U Oxford)

RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning
Riccardo Del Chiaro (University of Florence) · Bartłomiej Twardowski (Computer Vision Center, UAB) · Andrew D Bagdanov (University of Florence) · Joost van de Weijer (Computer Vision Center Barcelona)

One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers
Heng Yang (MIT) · Luca Carlone (Massachusetts Institute of Technology)

f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning
Xin Zhang (Worcester Polytechnic Institute) · Yanhua Li ("Worcester Polytechnic Institute, USA") · Ziming Zhang (Worcester Polytechnic Institute) · Zhi-Li Zhang (University of Minnesota)

Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model
Yiwei Shen (Rutgers University) · Pierre C Bellec (Rutgers)

Improved Guarantees for k-means++ and k-means++ Parallel
Konstantin Makarychev (Northwestern University) · Aravind Reddy (Northwestern University) · Liren Shan (Northwestern University)

Neural Anisotropy Directions
Guillermo Ortiz-Jimenez (EPFL) · Apostolos Modas (EPFL) · Seyed-Mohsen Moosavi-Dezfooli (ETH Zürich) · Pascal Frossard (EPFL)

Efficient Low Rank Gaussian Variational Inference for Neural Networks
Marcin Tomczak (University of Cambridge) · Siddharth Swaroop (University of Cambridge) · Richard E Turner (University of Cambridge)

Predictive inference is free with the jackknife+-after-bootstrap
Byol Kim (University of Chicago) · Chen Xu (University of Chicago) · Rina Foygel Barber (University of Chicago)

Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses
Yihan Zhou (University of British Columbia) · Victor Sanches Portella (University of British Columbia) · Mark Schmidt (University of British Columbia) · Nicholas Harvey (University of British Columbia)

Hold me tight! Influence of discriminative features on deep network boundaries
Guillermo Ortiz-Jimenez (EPFL) · Apostolos Modas (EPFL) · Seyed-Mohsen Moosavi-Dezfooli (ETH Zürich) · Pascal Frossard (EPFL)

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
Xin Liu (University of Washington ) · Josh Fromm (OctoML) · Shwetak Patel (University of Washington) · Daniel McDuff (Microsoft Research)

Learning Mutational Semantics
Brian Hie (Massachusetts Institute of Technology) · Ellen Zhong (Massachusetts Institute of Technology) · Bryan Bryson (Massachusetts Institute of Technology) · Bonnie Berger (MIT)

Make One-Shot Video Object Segmentation Efficient Again
Tim Meinhardt (TUM) · Laura Leal-Taixé (TUM)

Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology
Quynh N Nguyen (Saarland University) · Marco Mondelli (IST Austria)

Uncertainty-aware Self-training for Text Classification with Few Labels
Subhabrata Mukherjee (Microsoft Research) · Ahmed Awadallah (Microsoft)

A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs
Nevena Lazic (DeepMind) · Dong Yin (DeepMind) · Mehrdad Farajtabar (DeepMind) · Nir Levine (DeepMind) · Dilan Gorur () · Chris Harris (Google) · Dale Schuurmans (Google Brain & University of Alberta)

Heavy-tailed Representations, Text Polarity Classification & Data Augmentation
Hamid JALALZAI (Télécom ParisTech) · Pierre Colombo (Telecom ParisTech) · Chloé Clavel (Telecom-ParisTech, Paris, France) · Eric Gaussier (Université Joseph Fourier, Grenoble) · Giovanna Varni (Telecom ParisTec) · Emmanuel Vignon (IBM) · Anne Sabourin (LTCI, Telecom ParisTech, Université Paris-Saclay)

Meta-Learning through Hebbian Plasticity in Random Networks
Elias Najarro (IT University of Copenhagen) · Sebastian Risi (IT University of Copenhagen)

Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Marwa El Halabi (MIT) · Slobodan Mitrović (MIT) · Ashkan Norouzi-Fard (Google Research) · Jakab Tardos (EPFL) · Jakub Tarnawski (Microsoft Research)

Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
Qian Lou (Indiana University Bloomington) · Bo Feng (Indiana university) · Geoffrey Charles Fox (Indiana University) · Lei Jiang (Indiana University Bloomington)

Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients
William Moses (MIT) · Valentin Churavy (Massachussets Institute of Technology)

COT-GAN: Generating Sequential Data via Causal Optimal Transport
Tianlin Xu (London School of Economics and Political Science) · Wenliang Le (Gatsby Unit, UCL) · Michael Munn (Google) · Beatrice Acciaio (London School of Economics)

Fine-Grained Dynamic Head for Object Detection
Lin Song (Xi'an Jiaotong University) · Yanwei Li (The Chinese University of Hong Kong) · Zhengkai Jiang (Institute of Automation,Chinese Academy of Sciences) · Zeming Li (Megvii(Face++) Inc) · Hongbin Sun (Xi'an Jiaotong University) · Jian Sun (Megvii, Face++) · Nanning Zheng (Xi'an Jiaotong University)

Riemannian Continuous Normalizing Flows
Emile Mathieu (University of Oxford) · Maximilian Nickel (Facebook AI Research)

Unsupervised Data Augmentation for Consistency Training
Qizhe Xie (CMU, Google Brain) · Zihang Dai (Carnegie Mellon University) · Eduard Hovy (CMU) · Thang Luong (Google Brain) · Quoc V Le (Google)

Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data
Aude Sportisse (Sorbonne University, Ecole Polytechnique) · Claire Boyer (LPSM, Sorbonne Université) · Julie Josses (CMAP / CNRS)

Neuron Shapley: Discovering the Responsible Neurons
Amirata Ghorbani (Stanford University) · James Zou (Stanford University)

On Learning Ising Models under Huber's Contamination Model
Adarsh Prasad (Carnegie Mellon University) · Vishwak Srinivasan (Carnegie Mellon University) · Sivaraman Balakrishnan (Carnegie Mellon University) · Pradeep Ravikumar (Carnegie Mellon University)

On the Equivalence between Online and Private Learnability beyond Binary Classification
Young H Jung (Microsoft) · Baekjin Kim (University of Michigan) · Ambuj Tewari (University of Michigan)

A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
Wei Deng (Purdue University) · Guang Lin (Purdue University) · Faming Liang (Purdue University)

Learning from Label Proportions: A Mutual Contamination Framework
Clayton Scott (University of Michigan) · Jianxin Zhang (University of Michigan)

Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
Guoliang Kang (Carnegie Mellon University) · Yunchao Wei (UTS) · Yi Yang (UTS) · Yueting Zhuang (Zhejiang University) · Alexander Hauptmann (Carnegie Mellon University)

LoCo: Local Contrastive Representation Learning
Yuwen Xiong (Uber ATG / University of Toronto) · Mengye Ren (University of Toronto / Uber ATG) · Raquel Urtasun (Uber ATG)

What Makes for Good Views for Contrastive Representation Learning?
Yonglong Tian (MIT) · Chen Sun (Google Research) · Ben Poole (Google Brain) · Dilip Krishnan (Google) · Cordelia Schmid (Google) · Phillip Isola (Massachusetts Institute of Technology)

Deep Transformation-Invariant Clustering
Tom Monnier (École des ponts Paristech) · Thibault Groueix (École des ponts ParisTech) · Mathieu Aubry (École des ponts ParisTech)

Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
Alexander Ritchie (University of Michigan) · Robert Vandermeulen (Technische Universität Berlin) · Clayton Scott (University of Michigan)

When Do Neural Networks Outperform Kernel Methods?
Behrooz Ghorbani (Stanford University) · Song Mei (Stanford University) · Theodor Misiakiewicz (Stanford University) · Andrea Montanari (Stanford)

Truthful Data Acquisition via Peer Prediction
Yiling Chen (Harvard University) · Yiheng Shen (Tsinghua University) · Shuran Zheng (Harvard University)

Hedging in games: Faster convergence of external and swap regrets
Xi Chen (Columbia University) · Binghui Peng (Columbia University)

A mathematical theory of cooperative communication
Pei Wang (Rutgers University-Newark) · Junqi Wang (Rutgers University-Newark) · Pushpi Paranamana (Rutgers University-Newark) · Patrick Shafto (Rutgers University - Newark)

Detection as Regression: Certified Object Detection with Median Smoothing
Ping-yeh Chiang (University of Maryland, College Park) · Michael Curry (University of Maryland) · Ahmed Abdelkader (University of Maryland, College Park) · Aounon Kumar (University of Maryland, College Park) · John Dickerson (University of Maryland) · Tom Goldstein (University of Maryland)

Improving Neural Network Training in Low Dimensional Random Bases
Frithjof Gressmann (Graphcore) · Zach Eaton-Rosen (Graphcore) · Carlo Luschi (Graphcore)

Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
Antonio Barbalau (University of Bucharest) · Adrian Cosma (Politehnica University of Bucharest) · Radu Tudor Ionescu (University of Bucharest) · Marius Popescu (University of Bucharest)

Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games
Arun Suggala (Carnegie Mellon University) · Praneeth Netrapalli (Microsoft Research)

Deep Variational Instance Segmentation
Jialin Yuan (Oregon State University) · Chao Chen (Stony Brook University) · Fuxin Li (Oregon State University)

Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks
Junsouk Choi (Texas A&M University) · Robert Chapkin (Texas A&M University) · Yang Ni (Texas A&M University)

On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples
Richard Zhang (UIUC)

Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions
Tom Yan (Carnegie Mellon University) · Christian Kroer (Columbia University) · Alexander Peysakhovich (Facebook)

Learning Agent Representations for Ice Hockey
Guiliang Liu (Simon Fraser University) · Oliver Schulte (Simon Fraser University) · Pascal Poupart (University of Waterloo & RBC Borealis AI) · Mike Rudd (University of Waterloo) · Mehrsan Javan (SPORTLOGiQ)

Training Linear Finite-State Machines
Arash Ardakani (McGill University) · Amir Ardakani (McGill University) · Warren Gross (McGill University)

3D Self-Supervised Methods for Medical Imaging
Aiham Taleb (Hasso-Plattner-Institute, Potsdam University) · Winfried Loetzsch (Hasso Plattner Institute) · Noel Danz (HPI) · Julius Severin (HPI) · Thomas Gaertner (HPI) · Benjamin Bergner (HPI) · Christoph Lippert (Hasso Plattner Institute for Digital Engineering, Universität Potsdam)

On Numerosity of Deep Neural Networks
Xi Zhang (Shanghai Jiao Tong University) · Xiaolin Wu (McMaster University)

Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
Hongseok Namkoong (Stanford University) · Ramtin Keramati (Stanford University) · Steve Yadlowsky (Stanford University) · Emma Brunskill (Stanford University)

Walsh-Hadamard Variational Inference for Bayesian Deep Learning
Simone Rossi (EURECOM) · Sebastien Marmin (Department of Data Science, EURECOM) · Maurizio Filippone (EURECOM)

MCUNet: Tiny Deep Learning on IoT Devices
Ji Lin (MIT) · Wei-Ming Chen (National Taiwan University) · Yujun Lin (MIT) · john cohn (MIT-IBM Watson AI Lab, IBM Research) · Chuang Gan (MIT-IBM Watson AI Lab) · Song Han (MIT)

No-regret Learning in Price Competitions under Consumer Reference Effects
Negin Golrezaei (Google Research) · Patrick Jaillet (MIT) · Jason Cheuk Nam N Liang (MIT)

Avoiding the Midas Touch: Consequences of Misaligned AI
Simon Zhuang (UC Berkeley) · Dylan Hadfield-Menell (UC Berkeley)

Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Christopher Morris (Polytechnique Montreal) · Gaurav Rattan (RWTH Aachen University) · Petra Mutzel (University of Bonn)

Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models
Leonardo Cotta (Purdue University) · Carlos H. C. Teixeira (Universidade Federal de Minas Gerais) · Ananthram Swami (Army Research Laboratory, Adelphi) · Bruno Ribeiro (Purdue)

Improving Policy-Constrained Kidney Exchange via Pre-Screening
Duncan McElfresh (University of Maryland) · Michael Curry (University of Maryland) · Tuomas Sandholm (CMU, Strategic Machine, Strategy Robot, Optimized Markets) · John Dickerson (University of Maryland)

Self-Imitation Learning via Generalized Lower Bound Q-learning
Yunhao Tang (Columbia University)

Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Hanjun Dai (Google Brain) · Rishabh Singh (Google Brain) · Bo Dai (Google Brain) · Charles Sutton (Google) · Dale Schuurmans (Google Brain & University of Alberta)

The MAGICAL Benchmark for Robust Imitation
Sam Toyer (UC Berkeley) · Rohin Shah (UC Berkeley) · Andrew Critch (UC Berkeley) · Stuart Russell (UC Berkeley)

Weakly-Supervised Reinforcement Learning for Controllable Behavior
Lisa Lee (CMU / Google Brain / Stanford) · Ben Eysenbach (Carnegie Mellon University) · Russ Salakhutdinov (Carnegie Mellon University) · Shixiang (Shane) Gu (Google Brain) · Chelsea Finn (Stanford)

Efficient Algorithms for Device Placement of DNN Graph Operators
Jakub Tarnawski (Microsoft Research) · Amar Phanishayee (Microsoft Research) · Nikhil Devanur (Amazon) · Divya Mahajan (Microsoft) · Fanny Nina Paravecino (Microsoft)

Biased Stochastic Gradient Descent for Conditional Stochastic Optimization
Yifan Hu (University of Illinois at Urbana-Champaign) · Siqi Zhang (University of Illinois at Urbana-Champaign) · Xin Chen (University of Illinois at Urbana-Champaign) · Niao He (UIUC)

Modeling and Optimization Trade-off in Meta-learning
Katelyn Gao (Intel Labs) · Ozan Sener (Intel Labs)

Directional Pruning of Deep Neural Networks
Shih-Kang Chao (University of Missouri) · Zhanyu Wang (Purdue University) · Yue Xing (Purdue University) · Guang Cheng (Purdue University)

Structured Convolutions for Efficient Neural Network Design
Yash Bhalgat (Qualcomm AI Research) · Yizhe Zhang (Qualcomm AI Research) · Jamie Menjay Lin (Qualcomm AI Research) · Fatih Porikli (Qualcomm CR&D)

An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods
Yanli Liu (UCLA) · Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Tamer Basar (University of Illinois at Urbana-Champaign) · Wotao Yin (Alibaba US, DAMO Academy)

An Improved Analysis of Stochastic Gradient Descent with Momentum
Yanli Liu (UCLA) · Yuan Gao (Columbia University) · Wotao Yin (Alibaba US, DAMO Academy)

PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
Minh N Vu (University of Florida) · My T. Thai (University of Florida)

Neural Dynamic Policies for End-to-End Sensorimotor Learning
Shikhar Bahl (Carnegie Mellon University) · Mustafa Mukadam (Facebook AI Research) · Abhinav Gupta (Facebook AI Research/CMU) · Deepak Pathak (Carnegie Mellon University)

Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond
Gabriele Farina (Carnegie Mellon University) · Tuomas Sandholm (CMU, Strategic Machine, Strategy Robot, Optimized Markets)

Sparse Graphical Memory for Robust Planning
Misha Laskin (UC Berkeley) · Scott Emmons (UC Berkeley) · Ajay Jain (UC Berkeley) · Thanard Kurutach (University of California Berkeley) · Pieter Abbeel (UC Berkeley & covariant.ai) · Deepak Pathak (Carnegie Mellon University)

Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling
Tong Che (MILA) · Ruixiang ZHANG (Mila/UdeM) · Jascha Sohl-Dickstein (Google Brain) · Hugo Larochelle (Google Brain) · Liam Paull (Université de Montréal) · Yuan Cao (Google Brain) · Yoshua Bengio (Mila / U. Montreal)

Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time
Jerry Li (Microsoft) · Guanghao Ye (University of Washington)

VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
Jinsung Yoon (University of California, Los Angeles) · Yao Zhang (University of Cambridge) · James Jordon (University of Oxford) · Mihaela van der Schaar (University of Cambridge)

Learning Deep Attribution Priors Based On Prior Knowledge
Ethan Weinberger (University of Washington) · Joseph Janizek (University of Washington) · Su-In Lee (University of Washington)

NVAE: A Deep Hierarchical Variational Autoencoder
Arash Vahdat (NVIDIA) · Jan Kautz (NVIDIA)

MOReL: Model-Based Offline Reinforcement Learning
Rahul Kidambi (Cornell University) · Aravind Rajeswaran (University of Washington) · Praneeth Netrapalli (Microsoft Research) · Thorsten Joachims (Cornell)

Zap Q-Learning With Nonlinear Function Approximation
Shuhang Chen (University of Florida) · Adithya M Devraj (University of Florida) · Fan Lu (University of Florida) · Ana Busic (INRIA) · Sean Meyn (University of Florida)

Correlation Robust Influence Maximization
Louis Chen (Naval Postgraduate School) · Divya Padmanabhan (Singapore University of Technology and Design) · Chee Chin Lim (Singapore University of Technology and Design) · Karthik Natarajan (Singapore University of Technology and Design)

Structured Prediction for Conditional Meta-Learning
Ruohan Wang (Imperial College London) · Yiannis Demiris (Imperial College London) · Carlo Ciliberto (Imperial College London)

A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
Jinhyun So (University of Southern California) · Basak Guler (University of California, Riverside) · Salman Avestimehr (University of Southern California)

Planning with General Objective Functions: Going Beyond Total Rewards
Ruosong Wang (Carnegie Mellon University) · Peilin Zhong (Columbia University) · Simon Du (Institute for Advanced Study) · Russ Salakhutdinov (Carnegie Mellon University) · Lin Yang (UCLA)

Is Long Horizon RL More Difficult Than Short Horizon RL?
Ruosong Wang (Carnegie Mellon University) · Simon Du (Institute for Advanced Study) · Lin Yang (UCLA) · Sham Kakade (University of Washington & Microsoft Research)

Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension
Ruosong Wang (Carnegie Mellon University) · Russ Salakhutdinov (Carnegie Mellon University) · Lin Yang (UCLA)

GCN meets GPU: Decoupling “When to Sample” from “How to Sample”
Morteza Ramezani (Pennsylvania State University) · Weilin Cong (Pennsylvania State University) · Mehrdad Mahdavi (Pennsylvania State University) · Anand Sivasubramaniam (Penn State) · Mahmut Kandemir (Pennsylvania State University)

Implicit Graph Neural Networks
Fangda Gu (UC Berkeley) · Heng Chang (Tsinghua University) · Wenwu Zhu (Tsinghua University) · Somayeh Sojoudi (University of California, Berkeley) · Laurent El Ghaoui (UC Berkeley)

Learning Bounds for Risk-sensitive Learning
Jaeho Lee (KAIST) · Sejun Park (KAIST) · Jinwoo Shin (KAIST)

Shared Space Transfer Learning for analyzing multi-site fMRI data
Muhammad Yousefnezhad (University of Alberta) · Alessandro Selvitella (Purdue University Fort Wayne) · Daoqiang Zhang (Nanjing University of Aeronautics and Astronautics) · Andrew Greenshaw (University of Alberta) · Russell Greiner (University of Alberta)

Towards Problem-dependent Optimal Learning Rates
Yunbei Xu (Columbia University) · Assaf Zeevi (Columbia University)

Estimation of Skill Distribution from a Tournament
Ali Jadbabaie (MIT) · Anuran Makur (MIT) · Devavrat Shah (Massachusetts Institute of Technology)

Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks
Jy-yong Sohn (KAIST) · Dong-Jun Han (KAIST) · Beongjun Choi (KAIST) · Jaekyun Moon (Korea Advanced Institute of Science and Technology)

Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms
Pinar Ozisik (UMass Amherst) · Philip Thomas (University of Massachusetts Amherst)

Provably Robust Metric Learning
Lu Wang (Nanjing University) · Xuanqing Liu (University of California, Los Angeles) · Jinfeng Yi (JD Research) · Yuan Jiang (National Key lab for Novel Software Technology) · Cho-Jui Hsieh (UCLA)

Ultra-Low Precision 4-bit Training of Deep Neural Networks
Xiao Sun (IBM Thomas J. Watson Research Center) · Naigang Wang (IBM T. J. Watson Research Center) · Chia-Yu Chen (IBM research) · Jiamin Ni (IBM) · Ankur Agrawal (IBM Research) · Xiaodong Cui (IBM T. J. Watson Research Center) · Swagath Venkataramani (IBM Research) · Kaoutar El Maghraoui (IBM Research) · Vijayalakshmi (Viji) Srinivasan (IBM TJ Watson) · Kailash Gopalakrishnan (IBM Research)

Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
Victor Veitch (Columbia University) · Anisha Zaveri (Weill Cornell Medicine)

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
Xiang Zhang (Harvard University) · Marinka Zitnik (Harvard University)

One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL
Saurabh Kumar (Stanford University) · Aviral Kumar (UC Berkeley) · Sergey Levine (UC Berkeley) · Chelsea Finn (Stanford)

Wasserstein Distances for Stereo Disparity Estimation
Divyansh Garg (Cornell University) · Yan Wang (Cornell) · Bharath Hariharan (Cornell University) · Mark Campbell (Cornell University) · Kilian Weinberger (Cornell University / ASAPP Research) · Wei-Lun Chao (Ohio State University (OSU))

Efficient Clustering for Stretched Mixtures: Landscape and Optimality
Kaizheng Wang (Columbia University) · Yuling Yan (Princeton University) · Mateo Diaz (Cornell University)

Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud
SEOHYUN KIM (Seoul National University) · JaeYoo Park (Seoul National University) · Bohyung Han (Seoul National University)

Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Kaixiong Zhou (Texas A&M University) · Xiao Huang (The Hong Kong Polytechnic University) · Yuening Li (Texas A&M University) · Daochen Zha (Texas A&M University) · Rui Chen (Samsung Research America) · Xia Hu (Texas A&M University)

Improving Auto-Augment via Augmentation-Wise Weight Sharing
Keyu Tian (Sensetime; Beihang University) · CHEN LIN (SenseTime) · Ming Sun (SenseTime Group Limited) · Luping Zhou (University of Sydney) · Junjie Yan (Sensetime Group Limited) · Wanli Ouyang (The University of Sydney)

Rotated Binary Neural Network
Mingbao Lin (Xiamen University) · Rongrong Ji (Xiamen University, China) · Zihan Xu (Xiamen University, China) · Baochang Zhang (Beihang University) · Yan Wang (Pinterest) · Yongjian Wu (Tencent Technology (Shanghai) Co.,Ltd) · Feiyue Huang (Tencent) · Chia-Wen Lin (National Tsing Hua University)

Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time
Kai Han (University of Science and Technology of China) · zongmai Cao (University of Science and Technology of China) · Shuang Cui (University of Science and Technology of China) · Benwei Wu (University of Science and Technology of China)

Network size and size of the weights in memorization with two-layers neural networks
Sebastien Bubeck (Microsoft Research) · Ronen Eldan (Weizmann) · Yin Tat Lee (UW) · Dan Mikulincer (Institute Weizmann)

Deep Imitation Learning for Bimanual Robotic Manipulation
Fan Xie (Northeastern University) · Alexander Chowdhury (Northeastern University) · M. Clara De Paolis Kaluza (Northeastern University) · Linfeng Zhao (Northeastern University) · Lawson Wong (Northeastern University) · Rose Yu (University of California, San Diego)

Learning to Mutate with Hypergradient Guided Population
Zhiqiang Tao (Santa Clara University) · Yaliang Li (Alibaba Group) · Bolin Ding ("Data Analytics and Intelligence Lab, Alibaba Group") · Ce Zhang (ETH Zurich) · Jingren Zhou (Alibaba Group) · Yun Fu (Northeastern University)

The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification
Yihao Lv (Zhejiang University) · Youzhi Gu (Zhejiang University) · Liu Xinggao (Zhejiang University)

Submodular Meta-Learning
Arman Adibi (University of Pennsylvania) · Aryan Mokhtari (UT Austin) · Hamed Hassani (UPenn)

COPT: Coordinated Optimal Transport on Graphs
Yihe Dong (Microsoft) · Will Sawin (Columbia University)

Less is More: A Deep Graph Metric Learning Perspective Using Few Proxies
Yuehua Zhu (Xidian University) · Muli Yang (Xidian University) · Cheng Deng (Xidian University) · Wei Liu (Tencent AI Lab)

Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
Yuxi Li (Shanghai Jiao Tong University) · Jinlong Peng (Tencent Youtu Lab) · Ning Xu (Adobe Research) · John See (Multimedia University) · Weiyao Lin (Shanghai Jiao Tong university)

High-Throughput Synchronous Deep RL
Iou-Jen Liu (University of Illinois at Urbana-Champaign) · Raymond Yeh (University of Illinois at Urbana–Champaign) · Alexander Schwing (University of Illinois at Urbana-Champaign)

Learning to Learn with Feedback and Local Plasticity
Jack Lindsey (Columbia University) · Ashok Litwin-Kumar (Columbia University)

A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions
Yulong Lu (Duke University) · Jianfeng Lu (Duke University)

Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems
Aman Sinha (Stanford University) · Matthew O'Kelly (University of Pennsylvania) · Russ Tedrake (MIT) · John Duchi (Stanford)

Classification with Valid and Adaptive Coverage
Yaniv Romano (Stanford University) · Matteo Sesia (Stanford) · Emmanuel Candes (Stanford University)

Learning Sparse Prototypes for Text Generation
Junxian He (Carnegie Mellon University) · Taylor Berg-Kirkpatrick (University of California San Diego) · Graham Neubig (Carnegie Mellon University)

Towards a Combinatorial Characterization of Bounded-Memory Learning
Alon Gonen (UCSD) · Shachar Lovett (University of California San Diego) · Michal Moshkovitz (University of California San Diego)

Detecting Interactions from Neural Networks via Topological Analysis
Zirui Liu (Texas A&M University) · Qingquan Song (Texas A&M University) · Kaixiong Zhou (Texas A&M University) · Ting-Hsiang Wang (Texas A&M University) · Ying Shan (Tencent) · Xia Hu (Texas A&M University)

Achieving Equalized Odds by Resampling Sensitive Attributes
Yaniv Romano (Stanford University) · Stephen Bates (Stanford University) · Emmanuel Candes (Stanford University)

SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds
Hyeongju Kim (Seoul National University) · Hyeonseung Lee (Seoul National University) · Woo Hyun Kang (Seoul National University) · Joun Yeop Lee (Seoul National University) · Nam Soo Kim (Seoul National University)

RepPoints v2: Verification Meets Regression for Object Detection
Yihong Chen (Peking University) · Zheng Zhang (MSRA) · Yue Cao (Microsoft Research) · Liwei Wang (Peking University) · Stephen Lin (Microsoft Research) · Han Hu (Microsoft Research Asia)

Entrywise convergence of iterative methods for eigenproblems
Vasileios Charisopoulos (Cornell University) · Austin Benson (Cornell University) · Anil Damle (Cornell University)

Learning Strategy-Aware Linear Classifiers
Yiling Chen (Harvard University) · Yang Liu (UC Santa Cruz) · Chara Podimata (Harvard University)

Functional Regularization for Representation Learning: A Unified Theoretical Perspective
Siddhant Garg (University of Wisconsin-Madison) · Yingyu Liang (University of Wisconsin Madison)

Learning to Communicate in Multi-Agent Systems via Transformer-Guided Program Synthesis
Jeevana Priya Inala (MIT) · Yichen Yang (MIT) · James Paulos (University of Pennsylvania) · Yewen Pu (MIT) · Osbert Bastani (University of Pennysylvania) · Vijay Kumar (University of Pennsylvania) · Martin Rinard (MIT) · Armando Solar-Lezama (MIT)

Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems
Songtao Lu (IBM Research) · Meisam Razaviyayn (University of Southern California) · Bo Yang (University of Minnesota) · Kejun Huang (University of Florida) · Mingyi Hong (University of Minnesota)

Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach
Luofeng Liao (University of Chicago) · You-Lin Chen (Department of Statistics, University of Chicago) · Zhuoran Yang (Princeton) · Bo Dai (Google Brain) · Mladen Kolar (University of Chicago) · Zhaoran Wang (Northwestern University)

A Study on Post-Hoc Methods for Debiasing Neural Networks
Yash Savani (RealityEngines.AI) · Colin White (RealityEngines.AI) · Naveen Sundar Govindarajulu (RealityEngines.AI)

Multi-Fidelity Bayesian Optimization via Deep Neural Networks
Shibo Li (University of Utah) · Wei Xing (University of Utah) · Robert Kirby (University of Utah) · Shandian Zhe (University of Utah)

Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information
Genevieve E Flaspohler (Massachusetts Institute of Technology) · Nicholas Roy (MIT) · John W Fisher III (MIT)

Semi-Supervised Neural Architecture Search
Renqian Luo (University of Science and Technology of China) · Xu Tan (Microsoft Research) · Rui Wang (Microsoft Research Asia) · Tao Qin (Microsoft Research) · Enhong Chen (University of Science and Technology of China) · Tie-Yan Liu (Microsoft Research Asia)

The route to chaos in routing games: When is price of anarchy too optimistic?
Thiparat Chotibut (Chulalongkorn university) · Fryderyk Falniowski (Cracow University of Economics) · Michał Misiurewicz (Indiana University-Purdue University Indianapolis) · Georgios Piliouras (Singapore University of Technology and Design)

Improved Schemes for Episodic Memory based Lifelong Learning Algorithm
Yunhui Guo (University of California, San Diego) · Mingrui Liu (Boston University) · Tianbao Yang (The University of Iowa) · Tajana Rosing (University of California, San Diego)

Diversity can be Transferred: Output Diversification for White- and Black-box Attacks
Yusuke Tashiro (Japan Digital Design) · Yang Song (Stanford University) · Stefano Ermon (Stanford)

Auto Learning Attention
Benteng Ma (Northwestern Polytechnical University) · Jing Zhang (The University of Sydney) · Yong Xia (Northwestern Polytechnical University, Research & Development Institute of Northwestern Polytechnical University in Shenzhen) · Dacheng Tao (University of Sydney)

Stochastic Stein Discrepancies
Jackson Gorham (Stanford University) · Anant Raj (Max Planck Institute for Intelligent Systems) · Lester Mackey (Microsoft Research)

The Power of Comparisons for Actively Learning Linear Classifiers
Max Hopkins (University of California San Diego) · Daniel Kane (UCSD) · Shachar Lovett (University of California San Diego)

On Adaptive Distance Estimation
Yeshwanth Cherapanamjeri (UC Berkeley) · Jelani Nelson (UC Berkeley)

Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality
Kwang-Sung Jun (U of Arizona) · Chicheng Zhang (University of Arizona)

Meta-learning from Tasks with Heterogeneous Attribute Spaces
Tomoharu Iwata (NTT) · Atsutoshi Kumagai (NTT Software Innovation Center)

Self-Distillation as Instance-Specific Label Smoothing
Zhilu Zhang (Cornell University) · Mert Sabuncu (Cornell)

Optimal Query Complexity of Secure Stochastic Convex Optimization
Wei Tang (Washington University in St.Louis) · Chien-Ju Ho (Washington University in St. Louis) · Yang Liu (UC Santa Cruz)

MomentumRNN: Integrating Momentum into Recurrent Neural Networks
Tan Nguyen (Rice University/UCLA) · Richard Baraniuk (Rice University) · Andrea Bertozzi (UCLA) · Stanley Osher (UCLA) · Bao Wang (UCLA)

Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards
Vrettos Moulos (UC Berkeley)

Bayesian Multi-type Mean Field Multi-agent Imitation Learning
Fan Yang (University at Buffalo) · Alina Vereshchaka (University at Buffalo) · Changyou Chen (University at Buffalo) · Wen Dong (University at Buffalo)

Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data
Michael Cogswell (Georgia Tech) · Jiasen Lu (Allen Institute of Artificial Intelligence ) · Rishabh Jain (Georgia Tech) · Stefan Lee (Oregon State University) · Devi Parikh (Georgia Tech / Facebook AI Research (FAIR)) · Dhruv Batra (Georgia Tech / Facebook AI Research (FAIR))

Hierarchical Granularity Transfer Learning
Shaobo Min (USTC) · Hongtao Xie (University of Science and Technology of China) · Hantao Yao ( Institute of Automation, Chinese Academy of Sciences) · Xuran Deng (University of Science and Technology of China) · Zheng-Jun Zha (University of Science and Technology of China) · Yongdong Zhang (University of Science and Technology of China)

Probabilistic Circuits for Variational Inference in Discrete Graphical Models
Andy Shih (Stanford University) · Stefano Ermon (Stanford)

Learning under Model Misspecification: Applications to Variational and Ensemble methods
Andres Masegosa (University of Almeria)

Self-training Avoids Using Spurious Features Under Domain Shift
Yining Chen (Stanford University) · Colin Wei (Stanford University) · Ananya Kumar (Stanford University) · Tengyu Ma (Stanford University)

Online Optimization with Memory and Competitive Control
Guanya Shi (Caltech) · Yiheng Lin (California Institute of Technology) · Soon-Jo Chung (Caltech) · Yisong Yue (Caltech) · Adam Wierman (California Institute of Technology)

Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search
Jaehyeon Kim (Kakao Enterprise) · Sungwon Kim (Seoul National University) · Jungil Kong (Kakao Enterprise) · Sungroh Yoon (Seoul National University)

Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
Kai Wang (Harvard University) · Bryan Wilder (Harvard University) · Andrew Perrault (Harvard University) · Milind Tambe (Harvard University/Google)

An implicit function learning approach for parametric modal regression
Yangchen Pan (University of Alberta) · Ehsan Imani (University of Alberta) · Martha White (University of Alberta) · Amir-massoud Farahmand (Vector Institute and University of Toronto)

Model-based Adversarial Meta-Reinforcement Learning
Zichuan Lin (Tsinghua University) · Garrett W. Thomas (Stanford University) · Guangwen Yang (Tsinghua University) · Tengyu Ma (Stanford University)

Falcon: Fast Spectral Inference on Encrypted Data
Qian Lou (Indiana University Bloomington) · Wen-jie Lu (Alibaba Group) · Cheng Hong (Alibaba Group) · Lei Jiang (Indiana University Bloomington)

Exemplar VAEs for Exemplar based Generation and Data Augmentation
Sajad Norouzi (University of Toronto / Vector Institute) · David J Fleet (University of Toronto) · Mohammad Norouzi (Google Brain)

Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization
Benjamin Aubin (Facebook AI) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL) · Yue Lu (Harvard University) · Lenka Zdeborová (University Paris-Saclay & EPFL)

Simple and Fast Algorithm for Binary Integer and Online Linear Programming
Xiaocheng Li (Department of Management Science and Engineering, Stanford University) · Chunlin Sun (Stanford University) · Yinyu Ye (Standord)

Searching Recurrent Architecture for Path-based Knowledge Graph Embedding
Yongqi Zhang (4Paradigm Inc.) · Quanming Yao (4paradigm) · Lei Chen (Hong Kong University of Science and Technology)

Provably Efficient Neural GTD for Off-Policy Learning
Hoi-To Wai (The Chinese University of Hong Kong) · Zhuoran Yang (Princeton) · Zhaoran Wang (Northwestern University) · Mingyi Hong (University of Minnesota)

On Correctness of Automatic Differentiation for Non-Differentiable Functions
Wonyeol Lee (Stanford University) · Hangyeol Yu (KAIST) · Xavier Rival (ENS) · Hongseok Yang (KAIST)

On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
Ye He (University of California, Davis) · Krishnakumar Balasubramanian (University of California, Davis) · Murat Erdogdu (University of Toronto)

MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
Sourav Biswas (University of Waterloo) · Jerry Liu (Uber ATG) · Kelvin Wong (University of Toronto) · Shenlong Wang (University of Toronto) · Raquel Urtasun (Uber ATG)

Universal Function Approximation on Graphs
Rickard Gabrielsson (Stanford University)

Efficient active learning of sparse halfspaces with arbitrary bounded noise
Chicheng Zhang (University of Arizona) · Jie Shen (Stevens Institute of Technology) · Pranjal Awasthi (Rutgers University/Google)

CASTLE: Regularization via Auxiliary Causal Graph Discovery
Trent Kyono (UCLA) · Yao Zhang (University of Cambridge) · Mihaela van der Schaar (University of Cambridge)

A Randomized Algorithm to Reduce the Support of Discrete Measures
Francesco Cosentino (University of Oxford) · Harald Oberhauser (University of Oxford) · Alessandro Abate (University of Oxford)

Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems
Luo Luo (The Hong Kong University of Science and Technology) · Haishan Ye (The Chinese University of Hong Kong, Shenzen) · Zhichao Huang (HKUST) · Tong Zhang (Tencent AI Lab)

Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods
Laurence Aitchison (University of Cambridge)

Learning from Aggregate Observations
Yivan Zhang (The University of Tokyo / RIKEN) · Nontawat Charoenphakdee (The University of Tokyo / RIKEN) · Zhenguo Wu (The University of Tokyo) · Masashi Sugiyama (RIKEN / University of Tokyo)

Model Inversion Networks for Model-Based Optimization
Aviral Kumar (UC Berkeley) · Sergey Levine (UC Berkeley)

Prediction with Corrupted Expert Advice
Idan Amir (Tel-Aviv University) · Idan Attias (Ben Gurion University) · Tomer Koren (Tel Aviv University & Google) · Yishay Mansour (Tel Aviv University / Google) · Roi Livni (Tel Aviv University)

Safe Reinforcement Learning via Curriculum Induction
Matteo Turchetta (ETH Zurich) · Andrey Kolobov (Microsoft Research) · Shital Shah (Microsoft) · Andreas Krause (ETH Zurich) · Alekh Agarwal (Microsoft Research)

Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
Nian Si (Stanford University) · Soumyadip Ghosh (IBM Research) · Jose Blanchet (Stanford University) · Mark Squillante (IBM Research)

Meta-Consolidation for Continual Learning
Joseph K J (Indian Institute of Technology Hyderabad) · Vineeth Nallure Balasubramanian (Indian Institute of Technology, Hyderabad)

Conservative Q-Learning for Offline Reinforcement Learning
Aviral Kumar (UC Berkeley) · Aurick Zhou (University of California, Berkeley) · George Tucker (Google Brain) · Sergey Levine (UC Berkeley)

Multiview Neural Surface Reconstruction with Implicit Lighting and Material
Lior Yariv (Weizmann Institute of Science) · Yoni Kasten (Weizmann Institute) · Dror Moran (Weizmann Institute of Science) · Meirav Galun (Weizmann Institute of Science) · Matan Atzmon (Weizmann Institute Of Science) · Basri Ronen (Weizmann Inst.) · Yaron Lipman (Weizmann Institute of Science)

Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev
Xiao Wang (Singapore university of technology and design) · Qi Lei (Princeton University) · Ioannis Panageas (UC Irvine)

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
Jungil Kong (Kakao Enterprise) · Jaehyeon Kim (Kakao Enterprise) · Jaekyoung Bae (Kakao Enterprise)

Practical Low-Rank Communication Compression in Decentralized Deep Learning
Thijs Vogels (EPFL) · Sai Praneeth Karimireddy (EPFL) · Martin Jaggi (EPFL)

Smooth And Consistent Probabilistic Regression Trees
Sami Alkhoury (University Grenoble Alpes) · Emilie Devijver (CNRS - UGA) · Marianne Clausel (IECL) · Myriam Tami (Université Paris-Saclay) · Eric Gaussier (Université Joseph Fourier, Grenoble) · georges Oppenheim (Private)

SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection
Xiaoya Li (Shannon.AI) · Yuxian Meng (Shannon.AI) · Mingxin Zhou (Shannon.AI) · Qinghong Han (Shannon.AI) · Fei Wu (Zhejiang University) · Jiwei Li (Shannon.AI)

Linear Disentangled Representations and Unsupervised Action Estimation
Matthew Painter (University of Southampton) · Adam Prugel-Bennett (apb@ecs.soton.ac.uk) · Jonathon Hare (University of Southampton)

Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering
Jingtao Ding (Tsinghua University) · Yuhan Quan (Tsinghua University) · Quanming Yao (4paradigm) · Yong Li (Tsinghua University) · Depeng Jin (Tsinghua University)

Interventional Few-Shot Learning
Zhongqi Yue (Nanyang Technological University) · Hanwang Zhang (NTU) · Qianru Sun (Singapore Management University) · Xian-Sheng Hua (Damo Academy, Alibaba Group)

Spike and slab variational Bayes for high dimensional logistic regression
Kolyan Ray (Imperial College London) · Botond Szabo (Leiden University) · Gabriel Clara (Vrije Universiteit Amsterdam)

Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Filip Hanzely (KAUST) · Slavomír Hanzely (KAUST) · Samuel Horváth (King Abdullah University of Science and Technology) · Peter Richtarik (KAUST)

BRP-NAS: Prediction-based NAS using GCNs
Thomas Chau (Samsung AI Center Cambridge) · Lukasz Dudziak (Samsung AI Center Cambridge) · Mohamed Abdelfattah (Samsung AI Centre Cambridge) · Royson Lee (Samsung AI Center Cambridge) · Hyeji Kim (Samsung AI Center Cambridge) · Nicholas Lane (Samsung AI Center Cambridge & University of Oxford)

Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network
Chaojie Wang (Xidian University) · Hao Zhang (Xidian University) · Bo Chen (Xidian University) · Dongsheng Wang (Xidian University) · Zhengjue Wang (Xidian University) · Mingyuan Zhou (University of Texas at Austin)

Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation
Yangxin Wu (Sun Yat-sen University) · Gengwei Zhang (Sun Yat-sen University) · Hang Xu (Huawei Noah's Ark Lab) · Xiaodan Liang (Sun Yat-sen University) · Liang Lin (Sun Yat-Sen University)

Variational Bayesian Monte Carlo with Noisy Likelihoods
Luigi Acerbi (University of Helsinki)

SuperLoss: A Generic Loss for Robust Curriculum Learning
Thibault Castells (Naver Labs) · Philippe Weinzaepfel (NAVER LABS Europe) · Jerome Revaud (Naver Labs Europe)

Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut
Shenfei Pei (Northwestern Polytechnical University) · Feiping Nie (University of Texas Arlington) · Rong Wang (Northwestern Polytechnical University) · Xuelong Li (Northwestern Polytechnical University)

Reservoir Computing meets Recurrent Kernels and Structured Transforms
Jonathan Dong (Laboratoire Kastler-Brossel) · Ruben Ohana (Ecole Normale Supérieure & LightOn) · Mushegh Rafayelyan (Kastler-Brossel Laboratory (ENS, Sorbonne U., PSL U., CNRS, Collège de France)) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL)

Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples
Jay Nandy (National University of Singapore) · Wynne Hsu (National University of Singapore) · Mong Li Lee (National University of Singapore)

Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation
KwanYong Park (KAIST) · Sanghyun Woo (KAIST) · Inkyu Shin (Korea Advanced Institute of Science and Technology) · In So Kweon (KAIST)

Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits
Pierre Perrault (INRIA - ENS Paris Saclay) · Etienne Boursier (ENS Paris Saclay) · Michal Valko (DeepMind) · Vianney Perchet (ENSAE & Criteo AI Lab)

Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Massimiliano Patacchiola (University of Edinburgh) · Jack Turner (University of Edinburgh) · Elliot J. Crowley (University of Edinburgh) · Michael O'Boyle (University of Edinburgh) · Amos Storkey (University of Edinburgh)

Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
Haim Kaplan (TAU, GOOGLE) · Yishay Mansour (Tel Aviv University / Google) · Uri Stemmer (Ben-Gurion University) · Eliad Tsfadia (Tel Aviv University and Google)

A Theoretical Framework for Target Propagation
Alexander Meulemans (ETH Zürich | University of Zürich | Institute of Neuroinformatics) · Francesco Carzaniga (Institute of Neuroinformatics, University of Zurich and ETH Zurich) · Johan Suykens (KU Leuven) · João Sacramento (ETH Zurich) · Benjamin F. Grewe (ETH Zurich)

Deep Shells: Unsupervised Shape Correspondence with Optimal Transport
Marvin Eisenberger (Technical University of Munich) · Aysim Toker (TUM) · Laura Leal-Taixé (TUM) · Daniel Cremers (Technical University of Munich)

The Implications of Local Correlation on Learning Some Deep Functions
Eran Malach (Hebrew University Jerusalem Israel) · Shai Shalev-Shwartz (Mobileye & HUJI)

Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model
Raphaël Berthier (INRIA, ENS) · Francis Bach (INRIA - Ecole Normale Superieure) · Pierre Gaillard ()

Autoregressive Score Matching
Chenlin Meng (Stanford University) · Lantao Yu (Stanford University) · Yang Song (Stanford University) · Jiaming Song (Stanford University) · Stefano Ermon (Stanford)

Quantile Propagation for Wasserstein-Approximate Gaussian Processes
Rui Zhang (The Australian National University) · Christian Walder (DATA61) · Edwin Bonilla (Data61) · Marian-Andrei Rizoiu (University of Technology Sydney) · Lexing Xie (Australian National University)

On the universality of deep learning
Emmanuel Abbe (Princeton University) · Colin Sandon (MIT)

Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs
Hao Tang (Shanghai Jiao Tong University) · Zhiao Huang (University of California San Diego) · Jiayuan Gu (University of California, San Diego) · Bao-Liang Lu (Shanghai Jiao Tong University) · Hao Su (UCSD)

Learning Parities with Neural Networks
Eran Malach (Hebrew University Jerusalem Israel) · Amit Daniely (Hebrew University and Google Research)

Neural Architecture Generator Optimization
Robin Ru (Oxford University) · Pedro M Esperança (Huawei Noah's Ark Lab, London) · Fabio Maria Carlucci (Huawei Noah's Ark Lab)

Time-Reversal Symmetric ODE Network
In Huh (Samsung Advanced Institute of Technology, Samsung Electronics) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics) · Sung Ju Hwang (KAIST, AITRICS) · Jinwoo Shin (KAIST)

CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection
Qijian Zhang (City University of Hong Kong) · Runmin Cong (Beijing Jiaotong University) · Junhui Hou (City University of Hong Kong, Hong Kong) · Chongyi Li ( Nanyang Technological University) · Yao Zhao (Beijing Jiaotong University)

Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study
Assaf Dauber (Tel-Aviv University) · Meir Feder (Tel-Aviv University) · Tomer Koren (Tel Aviv University & Google) · Roi Livni (Tel Aviv University)

Munchausen Reinforcement Learning
Nino Vieillard (Google Brain) · Olivier Pietquin (Google Research Brain Team) · Matthieu Geist (Google Brain)

Continual Deep Learning by Functional Regularisation of Memorable Past
Pingbo Pan (University of Technology Sydney) · Siddharth Swaroop (University of Cambridge) · Alexander Immer (EPFL) · Runa Eschenhagen (University of Osnabrueck) · Richard E Turner (University of Cambridge) · Mohammad Emtiyaz Khan (RIKEN, Tokyo)

Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking
Isidoros Tziotis (UT Austin) · Constantine Caramanis (UT Austin) · Aryan Mokhtari (UT Austin)

Joints in Random Forests
Alvaro Correia (Eindhoven University of Technology) · Robert Peharz (University of Cambridge) · Cassio de Campos (Eindhoven University of Technology)

Hierarchical Neural Architecture Search for Deep Stereo Matching
Xuelian Cheng (Monash University) · Yiran Zhong (Australian National University) · Mehrtash T Harandi (Monash University) · Yuchao Dai (Northwestern Polytechnical University) · Xiaojun Chang (Monash University) · Hongdong Li (Australian National University) · Tom Drummond (Monash University) · Zongyuan Ge (Monash University)

Decentralized Accelerated Proximal Gradient Descent
Haishan Ye (The Chinese University of Hong Kong, Shenzen) · Ziang Zhou (Fudan University) · Luo Luo (The Hong Kong University of Science and Technology) · Tong Zhang (Hong Kong University of Science and Technology)

Triple descent and the two kinds of overfitting: where & why do they appear?
Stéphane d'Ascoli (ENS / FAIR) · Levent Sagun () · Giulio Biroli (ENS)

BoxE: A Box Embedding Model for Knowledge Base Completion
Ralph Abboud (University of Oxford) · Ismail Ceylan (University of Oxford) · Thomas Lukasiewicz (University of Oxford) · Tommaso Salvatori (University of Oxford)

Graph Stochastic Neural Networks for Semi-supervised Learning
Haibo Wang (Tsinghua University) · Chuan Zhou (Chinese Academy of Sciences) · Xin Chen (Institute for Network Sciences and Cyberspace, Tsinghua University) · Jia Wu (Macquarie University) · Shirui Pan (Monash University) · Jilong Wang (Tsinghua University)

Restoring Negative Information in Few-Shot Object Detection
Yukuan Yang (Tsinghua University) · Fangyun Wei (Microsoft Research Asia) · Miaojing Shi (King's College London) · Guoqi Li (Tsinghua University)

A Non-Asymptotic Analysis for Stein Variational Gradient Descent
Anna Korba (Gatsby Unit - UCL) · Adil SALIM (KAUST) · Michael Arbel (UCL) · Giulia Luise (University College London) · Arthur Gretton (Gatsby Unit, UCL)

A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
Fan Wu (University of Oxford) · Patrick Rebeschini (University of Oxford)

An Unbiased Risk Estimator for Learning with Augmented Classes
Yu-Jie Zhang (Nanjing University) · Peng Zhao (Nanjing University) · Lanjihong Ma (Nanjing University) · Zhi-Hua Zhou (Nanjing University)

Fast Fourier Convolution
Lu Chi (Peking University) · Borui Jiang (Peking University) · Yadong Mu (Peking University)

A Self-Tuning Actor-Critic Algorithm
Tom Zahavy (Technion) · Zhongwen Xu (DeepMind) · Vivek Veeriah (University of Michigan) · Matteo Hessel (Google DeepMind) · Junhyuk Oh (DeepMind) · Hado van Hasselt (DeepMind) · David Silver (DeepMind) · Satinder Singh (DeepMind)

Real World Games Look Like Spinning Tops
Wojciech Czarnecki (DeepMind) · Gauthier Gidel (Mila) · Brendan Tracey (DeepMind) · Karl Tuyls (DeepMind) · Shayegan Omidshafiei (DeepMind) · David Balduzzi (XTX Markets) · Max Jaderberg (DeepMind)

Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space
Shangchen Du (SenseTime) · Shan You (SenseTime) · Xiaojie Li (sensetime) · Jianlong Wu (Shandong University) · Fei Wang (SenseTime) · Chen Qian (SenseTime) · Changshui Zhang (Tsinghua University)

Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
Feng Liu (Michigan State University) · Xiaoming Liu (Michigan State University)

Non-Crossing Quantile Regression for Distributional Reinforcement Learning
Fan Zhou (Shanghai University of Finance and Economics) · Jianing Wang (Shanghai University of Finance and Economics) · Xingdong Feng (Shanghai University of Finance and Economics)

Learning Implicit Credit Assignment for Multi-Agent Actor-Critic
Meng Zhou (University of Sydney) · Ziyu Liu (University of Sydney) · Pengwei Sui (University of Sydney) · Yixuan Li (The University of Sydney) · Yuk Ying Chung (The University of Sydney)

When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
Zhaozhi Qian (University of Cambridge) · Ahmed Alaa (UCLA) · Mihaela van der Schaar (University of Cambridge)

How hard is to distinguish graphs with graph neural networks?
Andreas Loukas (EPFL)

The Convolution Exponential and Generalized Sylvester Flows
Emiel Hoogeboom (University of Amsterdam) · Victor Garcia Satorras (University of Amsterdam) · Jakub Tomczak (Qualcomm AI Research) · Max Welling (University of Amsterdam / Qualcomm AI Research)

Variational Interaction Information Maximization for Cross-domain Disentanglement
HyeongJoo Hwang (Korea Advanced Institute of Science and Technology) · Geon-Hyeong Kim (KAIST) · Seunghoon Hong (KAIST) · Kee-Eung Kim (KAIST)

Towards Scalable Bayesian Learning of Causal DAGs
Jussi Viinikka (University of Helsinki) · Antti Hyttinen (University of Helsinki) · Johan Pensar (University of Oslo) · Mikko Koivisto (University of Helsinki)

Synthetic Data Generators -- Sequential and Private
Olivier Bousquet (Google Brain (Zurich)) · Roi Livni (Tel Aviv University) · Shay Moran (Google AI Princeton)

On Testing of Samplers
Kuldeep S Meel (National University of Singapore) · Yash Pralhad Pote (National University of Singapore) · Sourav Chakraborty (Indian Statistical Institute, India)

NanoFlow: scalable normalizing flows with sublinear parameter complexity
Sang-gil Lee (Seoul National University) · Sungwon Kim (Seoul National University) · Sungroh Yoon (Seoul National University)

Online Meta-Critic Learning for Off-Policy Actor-Critic Methods
Wei Zhou (National University of Defense Technology) · Yiying Li (National University of Defense Technology) · Yongxin Yang (University of Edinburgh ) · Huaimin Wang (National University of Defense Technology) · Timothy Hospedales (University of Edinburgh)

Dynamic Submodular Maximization
Morteza Monemizadeh (Technical University of Eindhoven)

Stationary Activations for Uncertainty Calibration in Deep Learning
Lassi Meronen (Aalto University) · Christabella Irwanto (Aalto University) · Arno Solin (Aalto University)

Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations
Sebastian Farquhar (University of Oxford) · Lewis Smith (University of Oxford) · Yarin Gal (University of Oxford)

VarGrad: A Low-Variance Gradient Estimator for Variational Inference
Lorenz Richter (Freie Universität Berlin, BTU Cottbus-Senftenberg, dida) · Ayman Boustati (University of Warwick) · Nikolas Nüsken (Universität Potsdam) · Francisco Ruiz (DeepMind) · Omer Deniz Akyildiz (University of Warwick)

Online Decision Based Visual Tracking via Reinforcement Learning
ke Song (Shandong university) · Wei Zhang (Shandong University) · Ran Song (School of Control Science and Engineering, Shandong University) · Yibin Li (Shandong University)

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Paul Barde (Quebec AI institute - Mila, McGill) · Julien Roy (Mila) · Wonseok Jeon (MILA, McGill University) · Joelle Pineau (McGill University) · Chris Pal (MILA, Polytechnique Montréal, Element AI) · Derek Nowrouzezahrai (McGill University)

Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian
Lorenzo Dall'Amico (GIPSA lab) · Romain Couillet (CentralSupélec) · Nicolas Tremblay (CNRS)

Discovering Reinforcement Learning Algorithms
Junhyuk Oh (DeepMind) · Matteo Hessel (Google DeepMind) · Wojciech Czarnecki (DeepMind) · Zhongwen Xu (DeepMind) · Hado van Hasselt (DeepMind) · Satinder Singh (DeepMind) · David Silver (DeepMind)

Small Nash Equilibrium Certificates in Very Large Games
Brian H Zhang (Carnegie Mellon University) · Tuomas Sandholm (CMU, Strategic Machine, Strategy Robot, Optimized Markets)

Influence Augmented Online Planning for Complex Environments
Jinke He (Delft University of Technology) · Miguel Suau (Delft University of Technology) · Frans Oliehoek (TU Delft)

Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping
Shashanka Ubaru (IBM T. J. Watson Research Center) · Sanjeeb Dash (IBM Research) · Arya Mazumdar (University of Massachusetts Amherst) · Oktay Gunluk (Cornell University)

Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework
Wanxin Jin (Purdue University) · Zhaoran Wang (Northwestern University) · Zhuoran Yang (Princeton) · Shaoshuai Mou (Purdue University)

Locally-Adaptive Nonparametric Online Learning
Ilja Kuzborskij (DeepMind) · Nicolò Cesa-Bianchi (Università degli Studi di Milano)

CO-Optimal Transport
Vayer Titouan (IRISA) · Ievgen Redko (Hubert Curien laboratory) · Rémi Flamary (Université Côte d'Azur) · Nicolas Courty (IRISA, Universite Bretagne-Sud)

On ranking via sorting by estimated expected utility
Clement Calauzenes (Criteo) · Nicolas Usunier (Facebook AI Research)

GANSpace: Discovering Interpretable GAN Controls
Erik Härkönen (Aalto University) · Aaron Hertzmann (Adobe) · Jaakko Lehtinen (Aalto University & NVIDIA) · Sylvain Paris (Adobe)

Learning Invariants through Soft Unification
Nuri Cingillioglu (Imperial College London) · Alessandra Russo (Imperial College London)

MULTI-STAGE INFLUENCE FUNCTION
Hongge Chen (MIT) · Si Si (Google Research) · Yang Li (Google) · Ciprian Chelba (Google) · Sanjiv Kumar (Google Research) · Duane Boning (Massachusetts Institute of Technology) · Cho-Jui Hsieh (UCLA)

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi (UWaterloo) · Julius von Kügelgen (MPI for Intelligent Systems, Tübingen & University of Cambridge) · Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen) · Isabel Valera (Max Planck Institute for Intelligent Systems)

Model-based Policy Optimization with Unsupervised Model Adaptation
Jian Shen (Shanghai Jiao Tong University) · Han Zhao (Carnegie Mellon University) · Weinan Zhang (Shanghai Jiao Tong University) · Yong Yu (Shanghai Jiao Tong Unviersity)

Causal analysis of Covid-19 Spread in Germany
Atalanti Mastakouri (Max Planck Institute for Intelligent Systems) · Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)

Optimally Deceiving a Learning Leader in Stackelberg Games
Georgios Birmpas (University of Oxford) · Jiarui Gan (University of Oxford) · Alexandros Hollender (University of Oxford) · Francisco Marmolejo (University of Oxford) · Ninad Rajgopal (University of Oxford) · Alexandros Voudouris (University of Essex)

Regularizing Black-box Models for Improved Interpretability
Gregory Plumb (Carnegie Mellon University) · Maruan Al-Shedivat (Carnegie Mellon University) · Ángel Alexander Cabrera (Carnegie Mellon University) · Adam Perer (Carnegie Mellon University) · Eric Xing (Petuum Inc. / Carnegie Mellon University) · Ameet Talwalkar (CMU)

Posterior Re-calibration for Imbalanced Datasets
Junjiao Tian (Georgia Institute of Technology) · Yen-Cheng Liu (Georgia Tech) · Nathaniel Glaser (Georgia Institute of Technology) · Yen-Chang Hsu (Georgia Institute of Technology) · Zsolt Kira (Georgia Institute of Techology)

Neutralizing Self-Selection Bias in Sampling for Sortition
Bailey Flanigan (Carnegie Mellon University) · Paul Goelz (Carnegie Mellon University) · Anupam Gupta (Carnegie Mellon University) · Ariel Procaccia (Harvard University)

Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Filippos Christianos (University of Edinburgh) · Lukas Schäfer (University of Edinburgh) · Stefano Albrecht (University of Edinburgh)

Learning Semantic-aware Normalization for Generative Adversarial Networks
Heliang Zheng (University of Science and Technology of China) · Jianlong Fu (Microsoft Research) · Yanhong Zeng (Sun Yat-sen University) · Zheng-Jun Zha (University of Science and Technology of China) · Jiebo Luo (U. Rochester)

Synthesizing Tasks for Block-based Programming
Umair Ahmed (National University of Singapore) · Maria Christakis (MPI-SWS) · Aleksandr Efremov (MPI-SWS) · Nigel Fernandez (MPI-SWS) · Ahana Ghosh (MPI-SWS) · Abhik Roychoudhury (National University of Singapore) · Adish Singla (MPI-SWS)

GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
Prune Truong (ETH Zurich) · Martin Danelljan (ETH Zurich) · Luc V Gool (Computer Vision Lab, ETH Zurich) · Radu Timofte (ETH Zurich)

Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings
Heejong Bong (Carnegie Mellon University) · Zongge Liu (Carnegie Mellon University) · Zhao Ren (University of Pittsburgh) · Matthew Smith (Carnegie Mellon University) · Valerie Ventura (Carnegie Mellon University) · Kass E Robert (CMU)

Balanced Meta-Softmax for Long-Tailed Visual Recognition
Ren Jiawei (Sensetime) · Cunjun Yu (NUS) · shunan sheng (SenseTime International Pte. Ltd.) · Xiao Ma (National University of Singapore) · Haiyu Zhao (SenseTime International Pte Ltd) · Shuai Yi (SenseTime Group Limited) · hongsheng Li (cuhk)

Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders
Masha Itkina (Stanford University) · Boris Ivanovic (Stanford University) · Ransalu Senanayake (Stanford University) · Mykel J Kochenderfer (Stanford University) · Marco Pavone (Stanford University)

Biological credit assignment through dynamic inversion of feedforward networks
William Podlaski (Champalimaud Research) · Christian K. Machens (Champalimaud Research)

A Variational Approach for Learning from Positive and Unlabeled Data
Hui Chen (Tongji University) · Fangqing Liu (Tongji University) · Yin Wang (Tongji University) · Liyue Zhao (Cloudwalk Inc.) · Hao Wu (Tongji University)

Phase retrieval in high dimensions: Statistical and computational phase transitions
Antoine Maillard (Ecole Normale Supérieure) · Bruno Loureiro (IPhT Saclay) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL) · Lenka Zdeborová (University Paris-Saclay & EPFL)

Neural Networks Learning and Memorization with (almost) no Over-Parameterization
Amit Daniely (Hebrew University and Google Research)

From Finite to Countable-Armed Bandits
Anand Kalvit (Columbia Business School) · Assaf Zeevi (Columbia University)

Constraining Variational Inference with Geometric Jensen-Shannon Divergence
Jacob Deasy (University of Cambridge) · Nikola Simidjievski (University of Cambridge) · Pietro Lió (University of Cambridge)

Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Liu (Google Research / Harvard) · Zi Lin (Google) · Shreyas Padhy (Google) · Dustin Tran (Google Brain) · Tania Bedrax Weiss (Google) · Balaji Lakshminarayanan (Google Brain)

Adversarial Learning for Robust Deep Clustering
Xu Yang (Xidian University) · Cheng Deng (Xidian University) · Kun Wei (Xidian University) · Junchi Yan (Shanghai Jiao Tong University) · Wei Liu (Tencent AI Lab)

Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations
Amit Daniely (Hebrew University and Google Research) · Hadas Shacham (Hebrew University)

Sub-sampling for Efficient Non-Parametric Bandit Exploration
Dorian Baudry (CNRS/INRIA) · Emilie Kaufmann (CNRS) · Odalric-Ambrym Maillard (INRIA)

Neuron Merging: Compensating for Pruned Neurons
Woojeong Kim (Korea Institute of Science and Technology) · Suhyun Kim (Korea Institute of Science and Technology) · Mincheol Park (Korea Institute of Science and Technology) · Geunseok Jeon (Korea Institute of Science and Technology)

Language and Visual Entity Relationship Graph for Agent Navigation
Yicong Hong (Australian National University) · Cristian Rodriguez (Australian National University) · Yuankai Qi (University of Adelaide ) · Qi Wu (University of Adelaide) · Stephen Gould (ANU)

Exchangeable Neural ODE for Set Modeling
Yang Li (UNC-Chapel Hill) · Haidong Yi (Department of Computer Science, UNC Chapel-Hill) · Christopher Bender (The University of North Carolina) · Siyuan Shan (UNC Chapel Hill) · Junier Oliva (UNC - Chapel Hill)

Learning Robust Decision Policies from Observational Data
Muhammad Osama (Uppsala University) · Dave Zachariah (Uppsala University) · Peter Stoica (Uppsala University)

Zero-Resource Knowledge-Grounded Dialogue Generation
Linxiao Li (Peking University) · Can Xu (microsoft) · Wei Wu (Meituan-Dianping Group) · YUFAN ZHAO (Microsoft) · Xueliang Zhao (Peking University) · Chongyang Tao (Microsoft)

A Combinatorial Perspective on Transfer Learning
Jianan Wang (DeepMind) · Eren Sezener (DeepMind) · David Budden (DeepMind) · Marcus Hutter (DeepMind) · Joel Veness (Deepmind)

Smoothly Bounding User Contributions in Differential Privacy
Alessandro Epasto (Google) · Mohammad Mahdian (Google Research) · Jieming Mao (Google Research) · Vahab Mirrokni (Google Research NYC) · Lijie Ren (Google)

Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs
Edouard Leurent (INRIA) · Odalric-Ambrym Maillard (INRIA) · Denis Efimov (Inria)

Recovery of sparse linear classifiers from mixture of responses
Venkata Gandikota (University of Massachusetts, Amherst) · Arya Mazumdar (University of Massachusetts Amherst) · Soumyabrata Pal (University of Massachusetts Amherst)

Graph Random Neural Networks for Semi-Supervised Learning on Graphs
Wenzheng Feng (Tsinghua University) · Jie Zhang (Webank Co.,Ltd) · Yuxiao Dong (Microsoft) · Yu Han (Tsinghua University) · Huanbo Luan (Tsinghua University) · Qian Xu (WeBank) · Qiang Yang (WeBank and HKUST) · Evgeny Kharlamov (Bosch Center for Artificial Intelligence) · Jie Tang (Tsinghua University)

Robust Multi-Object Matching via Iterative Reweighting of the Graph Connection Laplacian
Yunpeng Shi (Princeton University) · Shaohan Li (University of Minnesota) · Gilad Lerman (University of Minnesota)

Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization
Wei Wang (Southeast University) · Min-Ling Zhang (Southeast University)

CLEARER: Multi-Scale Neural Architecture Search for Image Restoration
Yuanbiao Gou (College of Computer Science, Sichuan University) · Boyun Li (College of Computer Science, Sichuan University) · Zitao Liu (TAL AI Lab) · Songfan Yang (TAL AI Lab) · Xi Peng (Institute for Infocomm, Research Agency for Science, Technology and Research (A*STAR) Singapore)

Learning Disentangled Representations of Videos with Missing Data
Armand Comas (Northeastern University) · Chi Zhang (Northeastern University) · Zlatan Feric (Northeastern University) · Octavia Camps (Northeastern University) · Rose Yu (University of California, San Diego)

Meta-trained agents implement Bayes-optimal agents
Vladimir Mikulik (Google DeepMind) · Grégoire Delétang (DeepMind) · Tom McGrath (Deepmind) · Tim Genewein (DeepMind) · Miljan Martic (DeepMind) · Shane Legg (DeepMind) · Pedro Ortega (DeepMind)

Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks
Lemeng Wu (UT Austin) · Bo Liu (University of Texas at Austin) · Qiang Liu (UT Austin) · Peter Stone (The University of Texas at Austin)

The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
Harm Van Seijen (Microsoft Research) · Hadi Nekoei (MILA) · Evan Racah (Mila, Université de Montréal) · Sarath Chandar (Mila / École Polytechnique de Montréal)

Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS
Han Shi (Hong Kong University of Science and Technology) · Renjie Pi (Huawei Noah’s Ark Lab) · Hang Xu (Huawei Noah's Ark Lab) · Zhenguo Li (Noah's Ark Lab, Huawei Tech Investment Co Ltd) · James Kwok (Hong Kong University of Science and Technology) · Tong Zhang (Hong Kong University of Science and Technology)

Digraph Inception Convolutional Networks
Zekun Tong (National University of Singapore) · Yuxuan Liang (National University of Singapore) · Changsheng Sun (National University of Singapore) · Xinke Li (National University of Singapore) · David Rosenblum (National University of Singapore) · Andrew Lim (National University of Singapore)

Adversarial Robustness of Supervised Sparse Coding
Jeremias Sulam (Johns Hopkins University) · Ramchandran Muthukumar (Johns Hopkins University) · Raman Arora (Johns Hopkins University)

Learning of Discrete Graphical Models with Neural Networks
Abhijith Jayakumar (Indian Institute of Science) · Andrey Lokhov (LANL) · Sidhant Misra (Los Alamos National Laboratory) · Marc Vuffray (Los Alamos National Laboratory)

Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks
Roman Pogodin (Gatsby Unit, University College London) · Peter E Latham (Gatsby Unit, UCL)

Accurate Optical Flow Estimation by Learned Matching Cost
Jianyuan Wang (Australian National University) · Yiran Zhong (Australian National University) · Yuchao Dai (Northwestern Polytechnical University) · Kaihao Zhang (Australian National University) · Pan Ji (NEC Labs) · Hongdong Li (Australian National University)

GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
Katja Schwarz (MPI Tuebingen) · Yiyi Liao (MPI Tuebingen) · Michael Niemeyer (Max Planck for Intelligent Systems) · Andreas Geiger (MPI-IS and University of Tuebingen)

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
Austin Tripp (University of Cambridge) · Erik Daxberger (University of Cambridge) · José Miguel Hernández-Lobato (University of Cambridge)

Graph Meta Learning via Local Subgraphs
Kexin Huang (Harvard University) · Marinka Zitnik (Harvard University)

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
Jihoon Tack (KAIST) · Sangwoo Mo (KAIST) · Jongheon Jeong (KAIST) · Jinwoo Shin (KAIST)

Efficient Learning of Generative Models via Finite-Difference Score Matching
Tianyu Pang (Tsinghua University) · Taufik Xu (Tsinghua University) · Chongxuan LI (Tsinghua University) · Yang Song (Stanford University) · Stefano Ermon (Stanford) · Jun Zhu (Tsinghua University)

Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations
Kevin Scaman (Noah's Ark Lab, Huawei Technologies) · Cedric Malherbe (Huawei Noah's Ark Lab)

Second Order PAC-Bayesian Bounds for the Weighted Majority Vote
Andres Masegosa (University of Almeria) · Stephan Lorenzen (University of Copenhagen) · Christian Igel (University of Copenhagen) · Yevgeny Seldin (University of Copenhagen)

Adversarial Distributional Training for Robust Deep Learning
Yinpeng Dong (Tsinghua University) · Zhijie Deng (Tsinghua University) · Tianyu Pang (Tsinghua University) · Hang Su (Tsinghua Univiersity) · Jun Zhu (Tsinghua University)

PAC-Bayesian Bound for the Conditional Value at Risk
Zakaria Mhammedi (The Australian National University and Data61) · Benjamin Guedj (Inria & University College London) · Robert Williamson (ANU)

Robust Sequence Submodular Maximization
Gamal A Sallam (Temple University) · Zizhan Zheng (Tulane University) · Jie Wu (Temple University) · Bo Ji (Virginia Tech)

Bi-level Score Matching for Learning Energy-based Latent Variable Models
Fan Bao (Tsinghua University) · Chongxuan LI (Tsinghua University) · Taufik Xu (Tsinghua University) · Hang Su (Tsinghua Univiersity) · Jun Zhu (Tsinghua University) · Bo Zhang (Tsinghua University)

Bootstrapping neural processes
Juho Lee (KAIST, AITRICS) · Yoonho Lee (AITRICS) · Jungtaek Kim (POSTECH) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics) · Sung Ju Hwang (KAIST, AITRICS) · Yee Whye Teh (University of Oxford, DeepMind)

A Normative Model of Local Supervision in Cortical Microcircuits
Siavash Golkar (Flatiron Institute) · David Lipshutz (Flatiron Institute) · Yanis Bahroun (Flatiron institute) · Anirvan Sengupta (Rutgers University) · Dmitri Chklovskii (Flatiron Institute, Simons Foundation)

WOR and $p$'s: Sketches for $\ell_p$-Sampling Without Replacement
Edith Cohen (Google) · Rasmus Pagh (IT University of Copenhagen) · David Woodruff (Carnegie Mellon University)

Estimating Training Data Influence by Tracking Gradient Descent
Garima Pruthi (Google) · Frederick Liu (Google Inc.) · Satyen Kale (Google) · Mukund Sundararajan (Google LLC)

On 1/n neural representation and robustness
Josue Nassar (Stony Brook University) · Piotr Sokol (Stony Brook University) · Sueyeon Chung (Columbia University) · Kenneth D Harris (UCL) · Il Memming Park (Stony Brook University)

Boosting Adversarial Training with Hypersphere Embedding
Tianyu Pang (Tsinghua University) · Xiao Yang (Tsinghua University) · Yinpeng Dong (Tsinghua University) · Taufik Xu (Tsinghua University) · Hang Su (Tsinghua Univiersity) · Jun Zhu (Tsinghua University)

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Patrick Lewis (Facebook AI Research) · Ethan Perez (New York University) · Aleksandra Piktus (Facebook AI) · Fabio Petroni (Facebook AI Research) · Vladimir Karpukhin (Facebook AI Research) · Naman Goyal (Facebook Inc) · Heinrich Küttler (Facebook AI Research) · Mike Lewis (Facebook AI Research) · Wen-tau Yih (Facebook AI Research) · Tim Rocktäschel (Facebook AI Research) · Sebastian Riedel () · Douwe Kiela (Facebook AI Research)

Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity
Gonçalo Correia (Instituto de Telecomunicações) · Vlad Niculae (Instituto de Telecomunicações) · Wilker Aziz (University of Amsterdam) · André Martins (Instituto de Telecomunicacoes (NIF: 502 854 200))

A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning
Bhavya Kailkhura (Lawrence Livermore National Lab) · Jayaraman J. Thiagarajan (Lawrence Livermore National Labs) · Qunwei Li (Ant Financial) · Jize Zhang (Lawrence Livermore National Laboratory) · Yi Zhou (University of Utah) · Timo Bremer (Lawrence Livermore National Laboratory)

OrganITE: Optimal organ transplants using an individual treatment effect
Jeroen Berrevoets (University of Cambridge) · James Jordon (University of Oxford) · Ioana Bica (University of Oxford) · alexander gimson (Cambridge University Hospitals) · Mihaela van der Schaar (University of Cambridge)

An analytic theory of shallow networks dynamics for hinge loss classification
Franco Pellegrini (École normale supérieure, Paris) · Giulio Biroli (ENS)

Consistent feature selection for analytic deep neural networks
Vu Dinh (University of Delaware) · Lam Ho (University of Dalhousie)

Guiding Deep Molecular Optimization with Genetic Exploration
Sung-Soo Ahn (KAIST) · Junsu Kim (KAIST) · Hankook Lee (Korea Advanced Institute of Science and Technology) · Jinwoo Shin (KAIST)

Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations
Mina Konakovic Lukovic (Massachusetts Institute of Technology) · Yunsheng Tian (Massachusetts Institute of Technology) · Wojciech Matusik (MIT)

Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction
Benoît Colange (CEA) · Jaakko Peltonen (University of Tampere) · Michael Aupetit (Qatar Computing Research Institute) · Denys Dutykh (CNRS) · Sylvain Lespinats (CEA Tech, INES, Annecy, France)

Debiased Contrastive Learning
Ching-Yao Chuang (MIT) · Joshua Robinson (MIT) · Yen-Chen Lin (MIT) · Antonio Torralba (MIT) · Stefanie Jegelka (MIT)

Relative gradient optimization of the Jacobian term in unsupervised deep learning
Luigi Gresele (MPI for Intelligent Systems, Tübingen) · Giancarlo Fissore (Inria) · Adrián Javaloy (Saarland University) · Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen) · Aapo Hyvarinen (University of Helsinki)

Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree
Peizhong Ju (Purdue University) · Xiaojun Lin (Purdue University) · Jia Liu (The Ohio State University)

Recurrent Random Networks as Optimized Kernel Machines
Sandra Nestler (Juelich Research Centre) · Christian Keup (Juelich Research Centre) · David Dahmen (Jülich Research Centre) · Matthieu Gilson (Juelich Forschungszentrum) · Holger Rauhut (RWTH Aachen University) · Moritz Helias (Juelich Research Centre)

Compact task representation as a normative model for higher-order brain activity
Severin Berger (Champalimaud Centre for the Unknown) · Christian K Machens (Champalimaud Centre for the Unknown)

Metric-Free Individual Fairness in Online Learning
Yahav Bechavod (Hebrew University of Jerusalem) · Christopher Jung (University of Pennsylvania) · Steven Wu (Carnegie Mellon University)

Empirical Likelihood for Contextual Bandits
Nikos Karampatziakis (Microsoft) · John Langford (Microsoft Research New York) · Paul Mineiro (Microsoft)

Deep Inverse Q-learning with Constraints
Gabriel Kalweit (University of Freiburg) · Maria Huegle (University of Freiburg) · Moritz Werling (BMWGroup, Unterschleissheim) · Joschka Boedecker (University of Freiburg)

Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis
Gang Wang (Beijing Institute of Technology) · Songtao Lu (IBM Research) · Georgios Giannakis (University of Minnesota) · Gerald Tesauro (IBM TJ Watson Research Center) · Jian Sun (Beijing Insitute of Technology)

Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Hyun-Suk Lee (Sejong University) · Yao Zhang (University of Cambridge) · William Zame (UCLA) · Cong Shen (University of Virginia) · Jang-Won Lee (Yonsei University) · Mihaela van der Schaar (University of Cambridge)

Fully Dynamic Algorithm for Constrained Submodular Optimization
Silvio Lattanzi (Google Research) · Slobodan Mitrović (MIT) · Ashkan Norouzi-Fard (Google Research) · Jakub Tarnawski (Microsoft Research) · Morteza Zadimoghaddam (Google Research)

Certifying Strategyproof Auction Networks
Michael Curry (University of Maryland) · Ping-yeh Chiang (University of Maryland, College Park) · Tom Goldstein (University of Maryland) · John Dickerson (University of Maryland)

Fast Transformers with Clustered Attention
Apoorv Vyas (Idiap Research Institute) · Angelos Katharopoulos (Idiap) · François Fleuret (University of Geneva)

Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs
Xiangru Huang (University of Texas at Austin) · Haitao Yang (University of Texas at Austin) · Etienne Vouga (The University of Texas at Austin) · Qixing Huang (The University of Texas at Austin)

Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
Matthew Faw (University of Texas at Austin) · Rajat Sen (Amazon) · Karthikeyan Shanmugam (IBM Research, NY) · Constantine Caramanis (UT Austin) · Sanjay Shakkottai (University of Texas at Austin)

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning
Nino Vieillard (Google Brain) · Tadashi Kozuno (Okinawa Institute of Science and Technology) · Bruno Scherrer (INRIA) · Olivier Pietquin (Google Research Brain Team) · Remi Munos (DeepMind) · Matthieu Geist (Google Brain)

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Ximeng Sun (Boston University) · Rameswar Panda (MIT-IBM Watson AI Lab) · Rogerio Feris (MIT-IBM Watson AI Lab, IBM Research) · Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)

Building powerful and equivariant graph neural networks with message-passing
Clément Vignac (EPFL) · Andreas Loukas (EPFL) · Pascal Frossard (EPFL)

Task-agnostic Exploration in Reinforcement Learning
Xuezhou Zhang (UW-Madison) · Yuzhe Ma (University of Wisconsin-Madison) · Adish Singla (MPI-SWS)

Compositional Visual Generation with Energy Based Models
Yilun Du (MIT) · Shuang Li (MIT) · Igor Mordatch (Google)

Distributionally Robust Parametric Maximum Likelihood Estimation
Viet Anh Nguyen (Stanford University) · Xuhui Zhang (Stanford University) · Jose Blanchet (Stanford University) · Angelos Georghiou (University of Cyprus)

Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices
John Duchi (Stanford) · Oliver Hinder (University of Pittsburgh) · Andrew Naber (Stanford University) · Yinyu Ye (Standord)

A Spectral Energy Distance for Parallel Speech Synthesis
Alexey Gritsenko (Google) · Tim Salimans (Google Brain Amsterdam) · Rianne van den Berg (Google Brain) · Jasper Snoek (Google Brain) · Nal Kalchbrenner (Google Brain)

Unsupervised Translation of Programming Languages
Baptiste Roziere (Facebook AI Research) · Marie-Anne Lachaux (Facebook AI Research) · Lowik Chanussot (Facebook AI Research) · Guillaume Lample (Facebook AI Research)

STEER : Simple Temporal Regularization For Neural ODE
Arnab Ghosh (University of Oxford) · Harkirat Behl (University of Oxford) · Emilien Dupont (Oxford University) · Philip Torr (University of Oxford) · Vinay Namboodiri (University of Bath)

Scalable and Consistent Estimation in Continuous-time Networks of Relational Events
Makan Arastuie (University of Toledo) · Subhadeep Paul (The Ohio State University) · Kevin Xu (University of Toledo)

An Ode to an ODE
Krzysztof Choromanski (Google Brain Robotics & Columbia University) · Jared Quincy Davis (Google Brain) · Valerii Likhosherstov (University of Cambridge) · Xingyou Song (Google Brain) · Vikas Sindhwani (Google) · Jean-Jacques Slotine (Massachusetts Institute of Technology) · Jacob Varley (Google) · Honglak Lee (Google Brain) · Adrian Weller (Cambridge, Alan Turing Institute)

Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
Tianren Zhang (Tsinghua University) · Shangqi Guo (Tsinghua University) · Tian Tan (Stanford University) · Xiaolin Hu (Tsinghua University) · Feng Chen (Tsinghua University)

Approximate Cross-Validation with Low-Rank Data in High Dimensions
William Stephenson (MIT) · Madeleine Udell (Cornell University) · Tamara Broderick (MIT)

Generative Modeling of Factorized Representations in Class-Imbalanced Data
Utkarsh Ojha (University of California, Davis) · Krishna Kumar Singh (University of California Davis) · Cho-Jui Hsieh (UCLA) · Yong Jae Lee (University of California, Davis)

High-contrast “gaudy” images improve the training of deep neural network models of visual cortex
Benjamin Cowley (Princeton University) · Jonathan W Pillow (Princeton University)

Online Multitask Learning with Long-Term Memory
Mark Herbster (University College London) · Stephen Pasteris (University College London) · Fai Yu Lisa Tse (University College London)

Unsupervised Learning of Object Landmarks via Self-Training Correspondence
Dimitrios Mallis (Computer Vision Laboratory - University of Nottingham) · Enrique Sanchez (Samsung AI Centre) · Matthew Bell (University of Nottingham) · Georgios Tzimiropoulos (Queen Mary University of London)

On Completeness-aware Concept-Based Explanations in Deep Neural Networks
Chih-Kuan Yeh (Carnegie Mellon University) · Been Kim (Google) · Sercan Arik (Google) · Chun-Liang Li (Google) · Tomas Pfister (Google) · Pradeep Ravikumar (Carnegie Mellon University)

Reinforcement Learning with Feedback Graphs
Christoph Dann (Carnegie Mellon University) · Yishay Mansour (Google) · Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research) · Ayush Sekhari (Cornell University) · Karthik Sridharan (Cornell University)

Can Graph Neural Networks Count Substructures?
Zhengdao Chen (New York University) · Lei Chen (New York University) · Soledad Villar (New York University) · Joan Bruna (NYU)

An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
Scott Fujimoto (McGill University) · David Meger (McGill University) · Doina Precup (McGill University / Mila / DeepMind Montreal)

Learning Neural Scene Representations of Multi-object Scenes With Multi-view Observations
Nanbo Li (University of Edinburgh) · C E (University of Edinburgh) · Robert Fisher (University of Edinburgh)

Minimal Regret in Online Recommendation Systems
Kaito Ariu (KTH) · Narae Ryu (KAIST) · Se-Young Yun (KAIST) · Alexandre Proutiere (KTH)

Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning
Jianda Chen (Nanyang Technological University) · Shangyu Chen (Nanyang Technological University, Singapore) · Sinno Jialin Pan (Nanyang Technological University, Singapore)

The Statistical Cost of Robust Kernel Hyperparameter Turning
Raphael Meyer (New York University) · Christopher Musco (New York University)

Cooperative Multi-Player Bandit Optimization
Ilai Bistritz (Stanford) · Nicholas Bambos (Stanford University)

On Second Order Behaviour in Augmented Neural ODEs
Alexander Norcliffe (University College London) · Cristian Bodnar (University of Cambridge) · Ben Day (University of Cambridge) · Nikola Simidjievski (University of Cambridge) · Pietro Lió (University of Cambridge)

Choice Bandits
Arpit Agarwal (University of Pennsylvania) · Nicholas Johnson (University of Pennsylvania) · Shivani Agarwal (University of Pennsylvania)

The phase diagram of approximation rates for deep neural networks
Dmitry Yarotsky (Skolkovo Institute of Science and Technology) · Anton Zhevnerchuk (Skolkovo Institute of Science and Technology)

The All-or-Nothing Phenomenon in Sparse Tensor PCA
Jonathan Niles-Weed (NYU) · Ilias Zadik (NYU)

A General Large Neighborhood Search Framework for Solving Integer Linear Programs
Jialin Song (Caltech) · ravi lanka (rakuten) · Yisong Yue (Caltech) · Bistra Dilkina (University of Southern California)

Causal Imitation Learning With Unobserved Confounders
Junzhe Zhang (Columbia University) · Daniel Kumor (Purdue University) · Elias Bareinboim (Columbia University)

Towards Safe Policy Improvement for Non-Stationary MDPs
Yash Chandak (University of Massachusetts Amherst) · Scott Jordan (University of Massachusetts Amherst) · Georgios Theocharous (Adobe Research) · Martha White (University of Alberta) · Philip Thomas (University of Massachusetts Amherst)

CSER: Communication-efficient SGD with Error Reset
Cong Xie (University of Illinois Urbana-Champaign) · Shuai Zheng (Amazon Web Services) · Oluwasanmi Koyejo (UIUC) · Indranil Gupta (UIUC) · Mu Li (Amazon) · Haibin Lin (Amazon Web Services)

Manifold structure in graph embeddings
Patrick Rubin-Delanchy (University of Bristol)

Sliding Window Algorithms for k-Clustering Problems
Michele Borassi (Google Switzerland GmbH) · Alessandro Epasto (Google) · Silvio Lattanzi (Google Research) · Sergei Vassilvitskii (Google) · Morteza Zadimoghaddam (Google Research)

Multi-Task Reinforcement Learning with Soft Modularization
Ruihan Yang (UC San Diego) · Huazhe Xu (UC Berkeley) · YI WU (UC Berkeley) · Xiaolong Wang (UCSD/UC Berkeley)

Listening to Sounds of Silence for Speech Denoising
Ruilin Xu (Columbia University) · Rundi Wu (Columbia University) · Yuko Ishiwaka (SoftBank Corp.) · Carl Vondrick (Columbia University) · Changxi Zheng (Columbia University)

Soft Contrastive Learning for Visual Localization
Janine Thoma (ETH Zurich) · Danda Pani Paudel (ETH Zürich) · Luc V Gool (Computer Vision Lab, ETH Zurich)

Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Remi Tachet des Combes (Microsoft Research Montreal) · Han Zhao (Carnegie Mellon University) · Yu-Xiang Wang (UC Santa Barbara) · Geoffrey Gordon (MSR Montréal & CMU)

Online Robust Regression via SGD on the l1 loss
Scott Pesme (EPFL) · Nicolas Flammarion (EPFL)

Gaussian Gated Linear Networks
David Budden (DeepMind) · Adam Marblestone () · Eren Sezener (DeepMind) · Tor Lattimore (DeepMind) · Gregory Wayne (Google DeepMind) · Joel Veness (Deepmind)

Deeply Learned Spectral Total Variation Decomposition
Tamara Grossmann (University of Cambridge) · Yury Korolev (University of Cambridge) · Guy Gilboa (Technion) · Carola Schoenlieb (Cambridge University)

Approximate Cross-Validation for Structured Models
Soumya Ghosh (IBM Research) · William Stephenson (MIT) · Tin D Nguyen (MIT) · Sameer Deshpande (Wharton Statistics) · Tamara Broderick (MIT)

Self-Supervised Few-Shot Learning on Point Clouds
Charu Sharma (Indian Institute of Technology Hyderabad) · Manohar Kaul (IITH)

Variational Amodal Object Completion
Huan Ling (University of Toronto, NVIDIA) · David Acuna (University of Toronto, NVIDIA) · Karsten Kreis (NVIDIA) · Seung Wook Kim (University of Toronto) · Sanja Fidler (University of Toronto)

The NetHack Learning Environment
Heinrich Küttler (Facebook AI Research) · Nantas Nardelli (University of Oxford) · Alexander Miller (Facebook AI Research) · Roberta Raileanu (NYU) · Marco Selvatici (Imperial College London) · Edward Grefenstette (Facebook AI Research & University College London) · Tim Rocktäschel (University College London Facebook AI Research)

Secretary and Online Matching Problems with Machine Learned Advice
Antonios Antoniadis (University of Cologne) · Themis Gouleakis (Max Planck Institute for Informatics) · Pieter Kleer (Max Planck Institute for Informatics) · Pavel Kolev (Max-Planck-Institut für Informatik)

Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
Lynton Ardizzone (Heidelberg University) · Radek Mackowiak (Robert Bosch GmbH) · Carsten Rother (University of Heidelberg) · Ullrich Köthe (University of Heidelberg)

Distributed Distillation for On-Device Learning
Ilai Bistritz (Stanford) · Ariana Mann (Stanford University) · Nicholas Bambos (Stanford University)

MRI Banding Removal via Adversarial Training
Aaron Defazio (Facebook AI Research) · Tullie Murrell (Facebook AI Research) · Michael Recht (New York University School of Medicine)

Learning with Differentiable Pertubed Optimizers
Quentin Berthet (Google Brain) · Mathieu Blondel (Google) · Olivier Teboul (Ecole Centrale Paris) · Marco Cuturi (Google Brain & CREST - ENSAE) · Jean-Philippe Vert (Google) · Francis Bach (INRIA - Ecole Normale Superieure)

Online Learning with Primary and Secondary Losses
Avrim Blum (Toyota Technological Institute at Chicago) · Han Shao (Toyota Technological Institute at Chicago)

Visual Illusions: Statistics-based Computational Model
Elad Hirsch (Technion) · Ayellet Tal (Technion)

Weighted QMIX: Improving Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid (University of Oxford) · Gregory Farquhar (University of Oxford) · Bei Peng (University of Oxford) · Shimon Whiteson (University of Oxford)

Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits
Arya Akhavan (ENSAE - IIT) · Massimiliano Pontil (IIT & UCL) · Alexandre Tsybakov (CREST, ENSAE)

Decision trees as partitioning machines to characterize their generalization properties
Jean-Samuel Leboeuf (Université Laval) · Frédéric LeBlanc (Université de Moncton) · Mario Marchand (Université Laval)

Attribution for Graph Neural Networks
Benjamin Sanchez-Lengeling (Google Research) · Jennifer Wei (Google Research) · Brian Lee (Google Inc.) · Emily Reif (Google) · Peter Wang (Columbia University) · Wesley Wei Qian (University of Illinois at Urbana-Champaign) · Kevin McCloskey (Google) · Lucy Colwell (Google) · Alexander Wiltschko (Google Brain)

The Devil is in the Detail: a Framework for Macroscopic Prediction via Microscopic Models
Yingxiang Yang (ByteDance) · Negar Kiyavash (École Polytechnique Fédérale de Lausanne) · Le Song (Georgia Institute of Technology) · Niao He (UIUC)

Better Set Representations For Relational Reasoning
Qian Huang (Cornell University) · Horace He (Cornell University) · Abhay Singh (Cornell University) · Yan Zhang (University of Southampton) · Ser Nam Lim (Facebook AI) · Austin Benson (Cornell University)

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
Elise van der Pol (University of Amsterdam) · Daniel Worrall (University of Amsterdam) · Herke van Hoof (University of Amsterdam) · Frans Oliehoek (TU Delft) · Max Welling (University of Amsterdam / Qualcomm AI Research)

The Statistical Complexity of Early-Stopped Mirror Descent
Tomas Vaskevicius (University of Oxford) · Varun Kanade (University of Oxford) · Patrick Rebeschini (University of Oxford)

Optimistic Dual Extrapolation for Non-monotone Variational Inequality
Chaobing Song (Tsinghua University) · Yichao Zhou (UC Berkeley) · Zhengyuan Zhou (Stanford University) · Yong Jiang (Tsinghua) · Yi Ma (UC Berkeley)

Distributionally Robust Local Non-parametric Conditional Estimation
Viet Anh Nguyen (Stanford University) · Fan Zhang (Stanford University) · Jose Blanchet (Stanford University) · Erick Delage (HEC Montréal) · Yinyu Ye (Standord)

Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability
Christopher Frye (Faculty) · Colin Rowat (University of Birmingham) · Ilya Feige (Faculty)

List-Decodable Mean Estimation via Iterative Multi-Fitering
Ilias Diakonikolas (UW Madison) · Daniel Kane (UCSD) · Daniel Kongsgaard (UCSD)

Sufficient dimension reduction for classification using principal optimal transport direction
Cheng Meng (Renmin University of China) · Jun Yu (Beijing Institute of Technology) · Jingyi Zhang (Tsinghua University) · Ping Ma (University of Georgia) · Wenxuan Zhong (University of Georgia)

On the Modularity of Hypernetworks
Tomer Galanti (Tel Aviv University) · Lior Wolf (Facebook AI Research)

On Uniform Convergence and Low-Norm Interpolation Learning
Lijia Zhou (University of Chicago) · D.J. Sutherland (TTI-Chicago) · Nati Srebro (TTI-Chicago)

Non-Convex SGD Learns Halfspaces with Adversarial Label Noise
Ilias Diakonikolas (UW Madison) · Vasilis Kontonis (University of Wisconsin-Madison) · Christos Tzamos (UW Madison) · Nikos Zarifis (University of Wisconsin-Madison)

Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding
Gergely Flamich (University of Cambridge) · Marton Havasi (University of Cambridge) · José Miguel Hernández-Lobato (University of Cambridge)

Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
Uchenna Akujuobi (King Abdullah University of Science and Technology) · Jun Chen (King Abdullah University of Science and Techonology) · Mohamed Elhoseiny (KAUST and Stanford University) · Michael Spranger (Sony) · Xiangliang Zhang (" King Abdullah University of Science and Technology, Saudi Arabia")

Learning Latent Space Energy-Based Prior Model
Bo Pang (University of California Los Angeles) · Tian Han (Stevens Institute of Technology) · Erik Nijkamp (UCLA) · Song-Chun Zhu (UCLA) · Ying Nian Wu (University of California, Los Angeles)

CoinDICE: Off-Policy Confidence Interval Estimation
Bo Dai (Google Brain) · Ofir Nachum (Google Brain) · Yinlam Chow (Google Research) · Lihong Li (Google Research) · Csaba Szepesvari (DeepMind / University of Alberta) · Dale Schuurmans (Google Brain & University of Alberta)

Leveraging predictions in smoothed online convex optimization via gradient-based algorithms
Yingying Li (Harvard University) · Na Li (Harvard University)

UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree
Kacper Kania (Wrocław University of Science and Technology) · Maciej Zieba (Wroclaw University of Science and Technology, Tooploox) · Tomasz Kajdanowicz (Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, PL8960005851)

Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
Nicolas Keriven (CNRS, GIPSA-lab) · Alberto Bietti (Inria) · Samuel Vaiter (CNRS)

Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning
Weishi Shi (Rochester Institute of Technology) · Xujiang Zhao (The University of Texas at Dallas) · Feng Chen (UT Dallas) · Qi Yu (Rochester Institute of Technology)

Ridge Riding: Finding diverse solutions by following eigenvectors of the Hessian
Jack Parker-Holder (University of Oxford) · Cinjon Resnick (NYU) · Luke Metz (Google Brain) · Hengyuan Hu (Facebook) · Adam Lerer (Facebook AI Research) · Alistair Letcher (None) · Alexander Peysakhovich (Facebook) · Aldo Pacchiano (UC Berkeley) · Jakob Foerster (Facebook AI Research)

An Operator View of Policy Gradient Methods
Dibya Ghosh (Google) · Marlos C. Machado (Google Brain) · Nicolas Le Roux (Google Brain)

Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming
Joey Huchette (Rice University) · Haihao Lu (University of Chicago) · Hossein Esfandiari (Google Research) · Vahab Mirrokni (Google Research NYC)

Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains
Ervine Zheng (Rochester Institute of Technology) · Qi Yu (Rochester Institute of Technology) · Rui Li (Rochester Institute of Technology) · Pengcheng Shi (rit) · Anne Haake (Rochester Institute of Technology)

Evolving Normalization-Activation Layers
Hanxiao Liu (Google Brain) · Andy Brock (DeepMind) · Karen Simonyan (DeepMind) · Quoc V Le (Google)

Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
Ben Usman (Boston University, Google AI) · Avneesh Sud (Google) · Nick Dufour (Google Research) · Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)

The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
Douwe Kiela (Facebook AI Research) · Hamed Firooz (Facebook) · Aravind Mohan (Facebook) · Vedanuj Goswami (Facebook) · Amanpreet Singh (Facebook) · Pratik Ringshia (Facebook) · Davide Testuggine (Facebook)

Heavily-Constrained Learning with Lagrange Multiplier Models
Harikrishna Narasimhan (Google Research) · Andrew Cotter (Google) · Yichen Zhou (Google) · Serena Wang (Google, UC Berkeley) · Wenshuo Guo (UC Berkeley)

Continuous Submodular Maximization: Beyond DR-Submodularity
Moran Feldman (University of Haifa) · Amin Karbasi (Yale)

Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach
Peter W Glynn (Stanford University) · Ramesh Johari (Stanford University) · Mohammad Rasouli (Stanford University)

A Topological Filter for Learning with Label Noise
Pengxiang Wu (Rutgers University) · Songzhu Zheng (Stony Brook University) · Mayank Goswami (Queens College of CUNY) · Dimitris Metaxas (Rutgers University) · Chao Chen (Stony Brook University)

Cross-validation Confidence Intervals for Test Error
Pierre Bayle (Princeton University) · Alexandre Bayle (Harvard University) · Lucas Janson (Harvard University) · Lester Mackey (Microsoft Research)

Learning Graph Structure with A Finite-State Automaton Layer
Daniel Johnson (Google Research, Brain Team) · Hugo Larochelle (Google Brain) · Daniel Tarlow (Google Brain)

Learning Dynamic Belief Graphs to Generalize on Text-Based Games
Ashutosh Adhikari (University of Waterloo) · Xingdi Yuan (Microsoft Research) · Marc-Alexandre Côté (Microsoft Research) · Mikuláš Zelinka (Charles University, Faculty of Mathematics and Physics) · Marc-Antoine Rondeau (Microsoft Research) · Romain Laroche (Microsoft Research) · Pascal Poupart (University of Waterloo & RBC Borealis AI) · Jian Tang (Mila) · Adam Trischler (Microsoft) · Will Hamilton (McGill)

On Efficiency in Hierarchical Reinforcement Learning
Zheng Wen (DeepMind) · Doina Precup (DeepMind) · Morteza Ibrahimi (DeepMind) · Andre Barreto (DeepMind) · Benjamin Van Roy (Stanford University) · Satinder Singh (DeepMind)

An empirical study of loss landscape geometry and evolution of the data-dependent Neural Tangent Kernel
Stanislav Fort (Stanford University / Google Research) · Gintare Karolina Dziugaite (Element AI) · Mansheej Paul (Stanford University) · Sepideh Kharaghani (Element AI) · Daniel Roy (Univ of Toronto & Vector) · Surya Ganguli (Stanford)

Designing and Learning Trainable Priors with Non-Cooperative Games
Bruno Lecouat (Inria) · Jean Ponce (Inria) · Julien Mairal (Inria)

The interplay between randomness and structure during learning in RNNs
Friedrich Schuessler (Technion) · Francesca Mastrogiuseppe (UCL) · Alexis Dubreuil (ENS) · Srdjan Ostojic (Ecole Normale Superieure) · Omri Barak (Technion - Israeli institute of technology)

Faithful Embeddings for Knowledge Base Queries
Haitian Sun (Google Research) · Andrew Arnold (Amazon) · Tania Bedrax Weiss (Google) · Fernando Pereira (Google) · William Cohen (Google AI)

Efficient Generation of Structured Objects with Constrained Adversarial Networks
Luca Di Liello (University of Trento) · Pierfrancesco Ardino (University of Trento) · Jacopo Gobbi (University of Trento) · Paolo Morettin (University of Trento) · Stefano Teso (University of Trento) · Andrea Passerini (Università degli Studi di Trento)

Node Embeddings and Exact Low-Rank Representations of Complex Networks
Sudhanshu Chanpuriya (University of Massachusetts Amherst) · Cameron Musco (Microsoft Research) · Konstantinos Sotiropoulos (Boston University) · Charalampos Tsourakakis (Boston University/ISI foundation)

Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Florian Wenzel (Google Research) · Jasper Snoek (Google Brain) · Dustin Tran (Google Brain) · Rodolphe Jenatton (Google Brain)

Variational Policy Gradient Method for Reinforcement Learning with General Utilities
Junyu Zhang (Princeton University) · Alec Koppel (U.S. Army Research Laboratory) · Amrit Singh Bedi (US Army Research Laboratory) · Csaba Szepesvari (DeepMind / University of Alberta) · Mengdi Wang (Princeton University)

A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods
Yue Wu (University of California, Los Angeles) · Weitong ZHANG (University of California, Los Angeles) · Pan Xu (University of California, Los Angeles) · Quanquan Gu (UCLA)

POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis
Weichao Mao (University of Illinois at Urbana-Champaign) · Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Qiaomin Xie (Cornell University) · Tamer Basar (University of Illinois at Urbana-Champaign)

Instance-wise Feature Grouping
Aria Masoomi (Northeastern University) · Chieh Wu (Northeastern) · Tingting Zhao (Northeastern University) · Zifeng Wang (Northeastern University) · Peter Castaldi (Brigham Women's Hospital) · Jennifer Dy (Northeastern University)

Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion
Qianqian Ma (Boston University) · Alex Olshevsky (Boston University)

On the linearity of large non-linear models: when and why is the tangent kernel constant
Chaoyue Liu (The Ohio State University) · Libin Zhu (UC San Diego) · Mikhail Belkin (UC San Diego)

Disentangling by Subspace Diffusion
David Pfau (DeepMind) · Irina Higgins (DeepMind) · Alex Botev () · Sébastien Racanière (Google DeepMind)

Deep Automodulators
Ari Heljakka (Aalto University) · Yuxin Hou (Aalto University) · Juho Kannala (Aalto University) · Arno Solin (Aalto University)

Neural Networks with Small Weights and Depth-Separation Barriers
Gal Vardi (Weizmann Institute of Science) · Ohad Shamir (Weizmann Institute of Science)

COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
Mohammadreza Zolfaghari (University of Freiburg) · Simon Ging (Uni Freiburg) · Hamed Pirsiavash (University of Maryland, Baltimore County) · Thomas Brox (University of Freiburg)

Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
Yufeng Zhang (Northwestern University) · Qi Cai (Northwestern University) · Zhuoran Yang (Princeton) · Yongxin Chen (Georgia Institute of Technology) · Zhaoran Wang (Northwestern University)

Value-driven Hindsight Modelling
Arthur Guez (DeepMind) · Fabio Viola (DeepMind) · Theophane Weber (DeepMind) · Lars Buesing (Google DeepMind) · Steven Kapturowski (Deepmind) · Doina Precup (DeepMind) · David Silver (DeepMind) · Nicolas Heess (Google DeepMind)

Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization
Hassan Mortagy (Georgia Institute of Technology) · Swati Gupta (Georgia Institute of Technology) · Sebastian Pokutta (Zuse Institute Berlin)

Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice
Shufan Wang (Binghamton University-SUNY) · Jian Li (Binghamton University-SUNY ) · Shiqiang Wang (IBM Research)

Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
Jianzhun Du (Harvard University) · Joseph Futoma (Harvard University) · Finale Doshi-Velez (Harvard)

GradAug: A New Regularization Method for Deep Neural Networks
TAOJIANNAN YANG (University of North Carolina at Charlotte) · Sijie Zhu (University of North Carolina at Charlotte) · Chen Chen (University of North Carolina at Charlotte)

The Origins and Prevalence of Texture Bias in Convolutional Neural Networks
Katherine Hermann (Stanford University) · Ting Chen (Google) · Simon Kornblith (Google Brain)

Online Bayesian Goal Inference for Boundedly Rational Planning Agents
Tan Zhi-Xuan (Massachusetts Institute of Technology) · Jordyn Mann (Massachusetts Institute of Technology) · Tom Silver (MIT) · Josh Tenenbaum (MIT) · Vikash Mansinghka (Massachusetts Institute of Technology)

Reducing Adversarially Robust Learning to Non-Robust PAC Learning
Omar Montasser (Toyota Technological Institute at Chicago) · Steve Hanneke (Toyota Technological Institute at Chicago) · Nati Srebro (TTI-Chicago)

Debugging Tests for Model Explanations
Julius Adebayo (MIT) · Michael Muelly (Stanford University) · Ilaria Liccardi (MIT) · Been Kim (Google)

Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters
Kaiyi Ji (The Ohio State University) · Jason Lee (Princeton University) · Yingbin Liang (The Ohio State University) · H. Vincent Poor (Princeton University)

Learning Affordance Landscapes for Interaction Exploration in 3D Environments
Tushar Nagarajan (UT Austin) · Kristen Grauman (University of Texas at Austin)

Improved Techniques for Training Score-Based Generative Models
Yang Song (Stanford University) · Stefano Ermon (Stanford)

Learning to Incentivize Other Learning Agents
Jiachen Yang (Georgia Institute of Technology) · Ang Li (DeepMind, Mountain View) · Mehrdad Farajtabar (DeepMind) · Peter Sunehag (Google - DeepMind) · Edward Hughes (DeepMind) · Hongyuan Zha (Georgia Tech)

Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction
Gen Li (Tsinghua University) · Yuting Wei (Carnegie Mellon University) · Yuejie Chi (CMU) · Yuantao Gu (Tsinghua University) · Yuxin Chen (Princeton University)

Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee
Jincheng Bai (Purdue University) · Qifan Song (Purdue University ) · Guang Cheng (Purdue University)

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Michael Dennis (University of California Berkeley) · Natasha Jaques (MIT) · Eugene Vinitsky (UC Berkeley) · Alexandre Bayen (UC Berkeley) · Stuart Russell (UC Berkeley) · Andrew Critch (UC Berkeley) · Sergey Levine (UC Berkeley)

Fourier Sparse Leverage Scores and Approximate Kernel Learning
Tamas Erdelyi (Texas A&M University) · Cameron Musco (Microsoft Research) · Christopher Musco (New York University)

Movement Pruning: Adaptive Sparsity by Fine-Tuning
Victor Sanh (Hugging Face 🤗) · Thomas Wolf (Hugging Face) · Alexander Rush (Cornell University)

Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes
Ayoub El Hanchi (McGill University) · David Stephens (McGill University)

Principal Neighbourhood Aggregation for Graph Nets
Gabriele Corso (University of Cambridge) · Luca Cavalleri (University of Cambridge) · Dominique Beaini (Invivo AI) · Pietro Liò (University of Cambridge) · Petar Veličković (DeepMind)

Information theoretic limits of learning a sparse rule
Clément Luneau (École Polytechnique Fédérale de Lausanne) · jean barbier (EPFL) · Nicolas Macris (EPFL)

Learning from Mixtures of Private and Public Populations
Raef Bassily (The Ohio State University) · Shay Moran (Google AI Princeton) · Anupama Nandi (The Ohio State University)

On Warm-Starting Neural Network Training
Jordan Ash (Microsoft Research) · Ryan Adams (Princeton University)

Large-Scale Adversarial Training for Vision-and-Language Representation Learning
Zhe Gan (Microsoft) · Yen-Chun Chen (Microsoft) · Linjie Li (Microsoft) · Chen Zhu (University of Maryland) · Yu Cheng (Microsoft) · Jingjing Liu (Microsoft)

Debiasing Averaged Stochastic Gradient Descent to handle missing values
Aude Sportisse (Sorbonne University, Ecole Polytechnique) · Claire Boyer (LPSM, Sorbonne Université) · Aymeric Dieuleveut (Ecole Polytechnique, IPParis) · Julie Josses (CMAP / CNRS)

Learning Differentiable Programs with Admissible Neural Heuristics
Ameesh Shah (UC Berkeley) · Eric Zhan (Caltech) · Jennifer Sun (Caltech) · Abhinav Verma (Rice University) · Yisong Yue (Caltech) · Swarat Chaudhuri (The University of Texas at Austin)

Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification
Francesca Mignacco (IPhT, CEA Saclay) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL) · Pierfrancesco Urbani (Institut de Physique Théorique) · Lenka Zdeborová (University Paris-Saclay & EPFL)

Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization
Xuefeng GAO (The Chinese University of Hong Kong) · Mert Gurbuzbalaban (Rutgers) · Lingjiong Zhu (Florida State University)

Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method
Simiao Ren (Duke University) · Willie Padilla (Duke University) · Jordan Malof (Duke University)

Statistical Guarantees of Distributed Nearest Neighbor Classification
Jiexin Duan (Purdue University) · Xingye Qiao (Binghamton University) · Guang Cheng (Purdue University)

Detecting Hands and Recognizing Physical Contact in the Wild
Supreeth Narasimhaswamy (Stony Brook University) · Trung Nguyen (VinAI) · Minh Hoai Nguyen (Stony Brook University)

Calibration of Shared Equilibria in General Sum Partially Observable Markov Games
Nelson Vadori (J.P. Morgan AI Research) · Sumitra Ganesh (JPMorgan - AI Research) · Prashant Reddy (JP Morgan) · Manuela Veloso (J.P. Morgan AI Research )

Investigating Gender Bias in Language Models Using Causal Mediation Analysis
Jesse Vig (Salesforce) · Sebastian Gehrmann (Harvard University) · Yonatan Belinkov (Technion) · Sharon Qian (Harvard) · Daniel Nevo (Tel Aviv University) · Yaron Singer (Harvard University) · Stuart Shieber (Harvard University)

The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning
Giulia Denevi (University College of London) · Massimiliano Pontil (IIT & UCL) · Carlo Ciliberto (Imperial College London)

Adaptive Probing Policies for Shortest Path Routing
Aditya Bhaskara (University of Utah) · Sreenivas Gollapudi (Google Research) · Kostas Kollias (Google Research) · Kamesh Munagala (Duke University)

Learning Rich Rankings
Arjun Seshadri (Stanford University) · Stephen Ragain (Twitter) · Johan Ugander (Stanford University)

Smoothed Analysis of Online and Differentially Private Learning
Nika Haghtalab (Cornell University) · Tim Roughgarden (Columbia University) · Abhishek Shetty (Cornell University)

Learning Policy with Compositional Generalizability using Self-Supervised Object Proposals
Jiayuan Gu (University of California, San Diego) · Tongzhou Mu (University of California, San Diego) · Zhiwei Jia (University of California, San Diego) · Hao Tang (Shanghai Jiao Tong University) · Hao Su (UCSD)

Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping
Eduard Gorbunov (Moscow Institute of Physics and Technology) · Marina Danilova (ICS RAS) · Alexander Gasnikov (Moscow Institute of Physics and Technology)

Efficient Contextual Bandits with Continuous Actions
Maryam Majzoubi (NYU Tandon) · Chicheng Zhang (University of Arizona) · Rajan Chari (Microsoft) · Akshay Krishnamurthy (Microsoft) · John Langford (Microsoft Research New York) · Aleksandrs Slivkins (Microsoft Research)

From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering
Ines Chami (Stanford University) · Albert Gu (Stanford) · Vaggos Chatziafratis (Stanford University, California) · Christopher Ré (Stanford)

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
Chen Liu (EPFL) · Mathieu Salzmann (EPFL) · Tao Lin (EPFL) · Ryota Tomioka (Microsoft Research Cambridge) · Sabine Süsstrunk (EPFL)

Graphon Neural Networks and the Transferability of Graph Neural Networks
Luana Ruiz (University of Pennsylvania) · Luiz Chamon (University of Pennsylvania) · Alejandro Ribeiro (University of Pennsylvania)

Online Bayesian Persuasion
Matteo Castiglioni (Politecnico di Milano) · Andrea Celli (Politecnico di Milano) · Alberto Marchesi (Politecnico di Milano) · Nicola Gatti (Politecnico di Milano)

Linearly Converging Error Compensated SGD
Eduard Gorbunov (Moscow Institute of Physics and Technology) · Dmitry Kovalev (KAUST) · Dmitry Makarenko (MIPT) · Peter Richtarik (KAUST)

Factorizable Graph Convolutional Networks
Yiding Yang (Stevens Institute of Technology) · Zunlei Feng (Zhejiang University) · Mingli Song (Zhejiang University) · Xinchao Wang (Stevens Institute of Technology)

Handling Missing Data with Graph Representation Learning
Jiaxuan You (Stanford University) · Xiaobai Ma (Stanford University) · Yi Ding (Stanford University) · Mykel J Kochenderfer (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

Distribution Matching for Crowd Counting
Boyu Wang (Stony Brook University) · Huidong Liu (Stony Brook University) · Dimitris Samaras (Stony Brook University) · Minh Hoai Nguyen (Stony Brook University)

Reinforcement Learning with Augmented Data
Misha Laskin (UC Berkeley) · Kimin Lee (UC Berkeley) · Adam Stooke (UC Berkeley) · Lerrel Pinto (New York University) · Pieter Abbeel (UC Berkeley & covariant.ai) · Aravind Srinivas (UC Berkeley)

Improved Sample Complexity for Incremental Autonomous Exploration in MDPs
Jean Tarbouriech (Facebook AI Research Paris & Inria Lille) · Matteo Pirotta (Facebook AI Research) · Michal Valko (DeepMind Paris and Inria Lille - Nord Europe) · Alessandro Lazaric (Facebook Artificial Intelligence Research)

Exactly Computing the Local Lipschitz Constant of ReLU Networks
Matt Jordan (UT Austin) · Alexandros Dimakis (University of Texas, Austin)

Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech
Shailee Jain (The University of Texas at Austin) · Vy Vo (Intel Corporation) · Shivangi Mahto (The University of Texas at Austin) · Amanda LeBel (The University of Texas at Austin) · Javier Turek (Intel Labs) · Alexander Huth (The University of Texas at Austin)

No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
Andrea Celli (Politecnico di Milano) · Alberto Marchesi (Politecnico di Milano) · Gabriele Farina (Carnegie Mellon University) · Nicola Gatti (Politecnico di Milano)

EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
Jiachen Li (University of California, Berkeley) · Fan Yang (University of California, Berkeley) · Masayoshi Tomizuka (University of California, Berkeley) · Chiho Choi (Honda Research Institute US)

Discovering conflicting groups in signed networks
Ruo-Chun Tzeng (KTH) · Bruno Ordozgoiti (Aalto University) · Aristides Gionis (KTH Royal Institute of Technology)

Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
Raef Bassily (The Ohio State University) · Vitaly Feldman (Google Brain) · Cristobal Guzman (PUC-Chile) · Kunal Talwar (Apple)

Geometric Exploration for Online Control
Orestis Plevrakis (Princeton University) · Elad Hazan (Princeton University)

Differentiable Top-k with Optimal Transport
Yujia Xie (Georgia Institute of Technology) · Hanjun Dai (Google Brain) · Minshuo Chen (Georgia Tech) · Bo Dai (Google Brain) · Tuo Zhao (Gatech) · Hongyuan Zha (Georgia Tech) · Wei Wei (Google Inc.) · Tomas Pfister (Google)

Autofocused oracles for model-based design
Clara Fannjiang (UC Berkeley) · Jennifer Listgarten (UC Berkeley)

See, Hear, Explore: Curiosity via Audio-Visual Association
Victoria Dean (Carnegie Mellon University) · Shubham Tulsiani (Facebook AI Research) · Abhinav Gupta (Facebook AI Research/CMU)

Byzantine Resilient Distributed Multi-Task Learning
Jiani Li (Vanderbilt University) · Waseem Abbas (Vanderbilt University) · Xenofon Koutsoukos (Vanderbilt University)

Identifying Mislabeled Data using the Area Under the Margin Ranking
Geoff Pleiss (Columbia University) · Tianyi Zhang (Cornell University & ASAPP Research) · Ethan Elenberg (ASAPP) · Kilian Weinberger (Cornell University / ASAPP Research)

Probabilistic Fair Clustering
Seyed Esmaeili (University of Maryland, College Park) · Brian Brubach (University of Maryland) · Leonidas Tsepenekas (University of Maryland) · John Dickerson (University of Maryland)

Online Non-Convex Optimization with Inexact Models
Amélie Héliou (Criteo AI Lab) · Matthieu Martin (Criteo) · Panayotis Mertikopoulos (CNRS (French National Center for Scientific Research)) · Thibaud J Rahier (INRIA)

Depth Uncertainty in Neural Networks
Javier Antoran (University of Cambridge) · James U Allingham (University of Cambridge) · José Miguel Hernández-Lobato (University of Cambridge)

Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective
Kaixuan Huang (Princeton University) · Yuqing Wang (Georgia Institute of Technology) · Molei Tao (Georgia Institute of Technology) · Tuo Zhao (Gatech)

Off-Policy Evaluation via the Regularized Lagrangian
Mengjiao Yang (Google) · Ofir Nachum (Google Brain) · Bo Dai (Google Brain) · Lihong Li (Google Research) · Dale Schuurmans (Google Brain & University of Alberta)

Strictly Batch Imitation Learning by Energy-based Distribution Matching
Daniel Jarrett (University of Cambridge) · Ioana Bica (University of Oxford) · Mihaela van der Schaar (University of Cambridge)

Simple, Scalable Sparse k-means by Feature Ranking
Zhiyue Zhang (Duke University) · Kenneth Lange (UCLA) · Jason Xu (Duke University)

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Geoff Pleiss (Columbia University) · Martin Jankowiak (Uber AI Labs) · David Eriksson (Facebook) · Anil Damle (Cornell University) · Jacob Gardner (University of Pennsylvania)

Coded Sequential Matrix Multiplication For Straggler Mitigation
Nikhil Krishnan Muralee Krishnan (University of Toronto) · Seyederfan Hosseini (University of Toronto) · Ashish Khisti (University of Toronto)

Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE
Ding Zhou (Columbia University) · Xue-Xin Wei (University of Pennsylvania)

Taming Discrete Integration via the Boon of Dimensionality
Jeffrey Dudek (Rice University) · Dror Fried (The Open University of Israel) · Kuldeep S Meel (National University of Singapore)

Distance Encoding -- Design Provably More Powerful GNNs for Structural Representation Learning
Pan Li (Stanford University - Purdue University) · Yanbang Wang (Stanford University) · Hongwei Wang (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

Agnostic Learning of a Single Neuron with Gradient Descent
Spencer Frei (UCLA) · Yuan Cao (UCLA) · Quanquan Gu (UCLA)

Design Space for Graph Neural Networks
Jiaxuan You (Stanford University) · Zhitao Ying (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

Adversarial Example Games
Joey Bose (McGill/MILA) · Gauthier Gidel (Mila) · Hugo Berard (MILA) · Andre Cianflone (Mila/McGill) · Pascal Vincent (Facebook and U. Montreal) · Simon Lacoste-Julien (Mila, Université de Montréal & SAIL Montreal) · Will Hamilton (McGill)

Differentiable Augmentation for Data-Efficient GAN Training
Shengyu Zhao (Tsinghua University) · Zhijian Liu (MIT) · Ji Lin (MIT) · Jun-Yan Zhu (MIT) · Song Han (MIT)

Toward the Fundamental Limits of Imitation Learning
Nived Rajaraman (University of California, Berkeley) · Lin Yang (UCLA) · Jiantao Jiao (University of California, Berkeley) · Kannan Ramchandran (UC Berkeley)

WoodFisher: Efficient Second-Order Approximation for Neural Network Compression
Sidak Pal Singh (EPFL) · Dan Alistarh (IST Austria & Neural Magic Inc.)

On the Theory of Transfer Learning: The Importance of Task Diversity
Nilesh Tripuraneni (UC Berkeley) · Michael Jordan (UC Berkeley) · Chi Jin (Princeton University)

On the training dynamics of deep networks with $L_2$ regularization
Aitor Lewkowycz (Google) · Guy Gur-Ari (Google)

Differentiable Causal Discovery from Interventional Data
Philippe Brouillard (Mila, Université de Montréal) · Sébastien Lachapelle (Mila) · Alexandre Lacoste (Element AI) · Simon Lacoste-Julien (Mila, Université de Montréal & SAIL Montreal) · Alexandre Drouin (Element AI)

Targeted Adversarial Perturbations for Monocular Depth Prediction
Alex Wong (University of Los Angeles, California) · Safa Cicek (UCLA) · Stefano Soatto (UCLA)

A Causal View on Robustness of Neural Networks
Cheng Zhang (Microsoft Research, Cambridge, UK) · Kun Zhang (CMU) · Yingzhen Li (Microsoft Research Cambridge)

Truncated Linear Regression in High Dimensions
Constantinos Daskalakis (MIT) · Dhruv Rohatgi (Massachusetts Institute of Technology) · Emmanouil Zampetakis (MIT)

Online Agnostic Boosting via Regret Minimization
Nataly Brukhim (Princeton University) · Xinyi Chen (Princeton University) · Elad Hazan (Princeton University) · Shay Moran (Google AI Princeton)

Supermasks in Superposition for Continual Learning
Mitchell Wortsman (University of Washington, Allen Institute for Artificial Intelligence) · Vivek Ramanujan (Allen Institute for Artificial Intelligence) · Rosanne Liu (ML Collective) · Aniruddha Kembhavi (Allen Institute for Artificial Intelligence (AI2)) · Mohammad Rastegari (University of Washington) · Jason Yosinski (ML Collective) · Ali Farhadi (University of Washington)

Optimal Learning from Verified Training Data
Nicholas Bishop (University of Southampton) · Long Tran-Thanh (University of Warwick) · Enrico Gerding (university of Southampton)

Counterexample-Guided Learning of Monotonic Neural Networks
Aishwarya Sivaraman (UCLA) · Golnoosh Farnadi (Mila) · Todd Millstein (UCLA) · Guy Van den Broeck (UCLA)

COBE: Contextualized Object Embeddings from Narrated Instructional Video
Gedas Bertasius (Facebook Research) · Lorenzo Torresani (Facebook AI)

Random Reshuffling is Not Always Better
Christopher De Sa (Cornell)

Learning Composable Energy Surrogates for PDE Order Reduction
Alex Beatson (Princeton University) · Jordan Ash (Microsoft Research) · Geoffrey Roeder (Princeton University X, Alphabet Inc.) · Tianju Xue (Princeton University) · Ryan Adams (Princeton University)

Learning the Geometry of Wave-Based Imaging
Konik R Kothari (University of Illinois at Urbana Champaign) · Maarten de Hoop (Rice University) · Ivan Dokmanić (University of Basel)

Robust Federated Learning: The Case of Affine Distribution Shifts
Amirhossein Reisizadeh (UC Santa Barbara) · Farzan Farnia (Stanford University) · Ramtin Pedarsani (UC Santa Barbara) · Ali Jadbabaie (MIT)

Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing
Arthur Delarue (MIT) · Ross Anderson (Google Research) · Christian Tjandraatmadja (Google)

Fast geometric learning with symbolic matrices
Jean Feydy (École Normale Supérieure) · Joan Glaunès (Université Paris 5) · Benjamin Charlier (University of Montpellier) · Michael Bronstein (Imperial College London / Twitter)

MOPO: Model-based Offline Policy Optimization
Tianhe Yu (Stanford University) · Garrett W. Thomas (Stanford University) · Lantao Yu (Stanford University) · Stefano Ermon (Stanford) · James Zou (Stanford University) · Sergey Levine (UC Berkeley) · Chelsea Finn (Stanford) · Tengyu Ma (Stanford University)

From Predictions to Decisions: Using Lookahead Regularization
Nir Rosenfeld (Harvard University) · Anna Hilgard (Harvard University) · Sai Srivatsa Ravindranath (Harvard University) · David Parkes (Harvard University)

Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?
Shen Yan (Michigan State University) · Yu Zheng (Michigan State University) · Wei Ao (Michigan State University) · Xiao Zeng (Michigan State University) · Mi Zhang (Michigan State University)

3D Shape Reconstruction from Vision and Touch
Edward Smith (McGill University) · Roberto Calandra (Facebook AI Research) · Adriana Romero (FAIR) · Georgia Gkioxari (Facebook) · David Meger (McGill University) · Jitendra Malik (University of California at Berkley) · Michal Drozdzal (FAIR)

Thunder: a Fast Coordinate Selection Solver for Sparse Learning
Shaogang Ren (Baidu Research, USA) · Weijie Zhao (Baidu Research) · Ping Li (Baidu Research USA)

Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis
Shaocong Ma (University of Utah) · Yi Zhou (University of Utah) · Shaofeng Zou (University at Buffalo, the State University of New York)

Pointer Graph Networks
Petar Veličković (DeepMind) · Lars Buesing (Google DeepMind) · Matthew Overlan (DeepMind) · Razvan Pascanu (Google DeepMind) · Oriol Vinyals (Google DeepMind) · Charles Blundell (DeepMind)

Constant-Expansion Suffices for Compressed Sensing with Generative Priors
Constantinos Daskalakis (MIT) · Dhruv Rohatgi (Massachusetts Institute of Technology) · Emmanouil Zampetakis (MIT)

Learning Physical Graph Representations from Visual Scenes
Daniel Bear (Stanford University) · Chaofei Fan (Stanford) · Damian Mrowca (Stanford University) · Yunzhu Li (MIT) · Seth Alter (MIT) · Aran Nayebi (Stanford University) · Jeremy Schwartz (MIT) · Li Fei-Fei (Stanford University & Google) · Jiajun Wu (Google) · Josh Tenenbaum (MIT) · Daniel Yamins (Stanford University)

Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Polina Kirichenko (New York University) · Pavel Izmailov (New York University) · Andrew Gordon Wilson (New York University)

Demixed shared component analysis of neural population data from multiple brain areas
Yu Takagi (University of Oxford) · Steven Kennerley ("Institute of Neurology - Sobell Dept., University College of London") · Jun-ichiro Hirayama (AIST/RIKEN AIP) · Laurence Hunt (University of Oxford)

How many samples is a good initial point worth?
Jialun Zhang (University of Illinois Urbana Champaign) · Richard Zhang (UIUC)

Learning Memory-Efficient Stable Linear Dynamical Systems for Prediction and Control
Georgios Mamakoukas (Northwestern University) · Orest Xherija (University of Chicago) · Todd Murphey (Northwestern Univ.)

Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization
Sam Hopkins () · Jerry Li (Microsoft) · Fred Zhang (UC Berkeley)

Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms
Alexander Wei (Harvard University) · Fred Zhang (UC Berkeley)

An Efficient Framework for Clustered Federated Learning
Avishek Ghosh (University of California, Berkeley) · Jichan Chung (University of California, Berkeley) · Dong Yin (DeepMind) · Kannan Ramchandran (UC Berkeley)

Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval
Stefano Sarao Mannelli (Institut de Physique Théorique) · Giulio Biroli (ENS) · Chiara Cammarota (King's College London) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL) · Pierfrancesco Urbani (Institut de Physique Théorique) · Lenka Zdeborová (University Paris-Saclay & EPFL)

Markovian Score Climbing: Variational Inference with KL(p||q)
Christian Naesseth (Columbia University) · Fredrik Lindsten (Linköping University) · David Blei (Columbia University)

Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery
Yingjie Wang (Huazhong Agricultural University) · Hong Chen (University of Texas at Arlington) · Feng Zheng (SUSTech) · Chen Xu (University of Ottawa) · Tieliang Gong (University of Ottawa) · Yanhong Chen ( Chinese Academy of Sciences)

IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
Yossi Arjevani (NYU) · Joan Bruna (NYU) · Bugra Can (Rutgers University) · Mert Gurbuzbalaban (Rutgers) · Stefanie Jegelka (MIT) · Hongzhou Lin (MIT)

Adversarial Blocking Bandits
Nicholas Bishop (University of Southampton) · Hau Chan (University of Nebraska-Lincoln) · Debmalya Mandal (Columbia University) · Long Tran-Thanh (University of Warwick)

Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions
Stefano Sarao Mannelli (Institut de Physique Théorique) · Eric Vanden-Eijnden (New York University) · Lenka Zdeborová (University Paris-Saclay & EPFL)

Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Theofanis Karaletsos (Uber AI Labs) · Thang Bui (Uber AI / University of Sydney)

The Generalization-Stability Tradeoff In Neural Network Pruning
Brian Bartoldson (Lawrence Livermore National Laboratory) · Ari Morcos (Facebook AI Research) · Adrian Barbu (Florida State University, USA) · Gordon Erlebacher (Florida State University)

Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons
Qianyi Li (Harvard University) · Cengiz Pehlevan (Harvard University)

The Cone of Silence: Speech Separation by Localization
Teerapat Jenrungrot (University of Washington) · Vivek Jayaram (University of Washington) · Steve Seitz (University of Washington) · Ira Kemelmacher-Shlizerman (University of Washington)

Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects
Zijun Gao (Stanford University) · Yanjun Han (Stanford University)

Predictive coding in balanced neural networks with noise, chaos and delays
Jonathan Kadmon (Stanford University) · Jonathan Timcheck (Stanford University) · Surya Ganguli (Stanford)

Reconsidering Generative Objectives For Counterfactual Reasoning
Danni Lu (Virginia Tech) · Chenyang Tao (Duke University) · Junya Chen (Duke U) · Fan Li (Duke University) · Feng Guo (Virginia Tech) · Lawrence Carin (Duke University)

Inference for Batched Bandits
Kelly W Zhang (Harvard University) · Lucas Janson (Harvard University) · Susan Murphy (Harvard University)

Rethinking Pre-training and Self-training
Barret Zoph (Google Brain) · Golnaz Ghiasi (Google) · Tsung-Yi Lin (Google Brain) · Yin Cui (Google) · Hanxiao Liu (Google Brain) · Ekin Dogus Cubuk (Google Brain) · Quoc V Le (Google)

Generalization Bound of Gradient Descent for Non-Convex Metric Learning
MINGZHI DONG (University College London) · Xiaochen Yang (University College London) · Rui Zhu (City, University of London) · Yujiang Wang (Imperial College London) · Jing-Hao Xue (University College London)

Acceleration with a Ball Optimization Oracle
Yair Carmon (Stanford University) · Arun Jambulapati (Stanford University) · Qijia Jiang (Stanford University) · Yujia Jin (Stanford University) · Yin Tat Lee (UW) · Aaron Sidford (Stanford) · Kevin Tian (Stanford University)

Fairness with Overlapping Groups; a Probabilistic Perspective
Forest Yang (UC Berkeley) · Mouhamadou M Cisse (KAUST) · Oluwasanmi Koyejo (UIUC)

Automatic Curriculum Learning through Value Disagreement
Yunzhi Zhang (Berkeley Artificial Intelligence Research Lab) · Pieter Abbeel (UC Berkeley & covariant.ai) · Lerrel Pinto (New York University)

Dynamic Regret of Policy Optimization in Non-stationary Environments
Yingjie Fei (Cornell University) · Zhuoran Yang (Princeton) · Zhaoran Wang (Northwestern University) · Qiaomin Xie (Cornell University)

Ensuring fairness beyond the training data
Debmalya Mandal (Columbia University) · Samuel Deng (Columbia University) · Suman Jana (Columbia University) · Jeannette Wing (Columbia University) · Daniel Hsu (Columbia University)

A Unified View of Label Shift Estimation
Saurabh Garg (CMU) · Yifan Wu (Carnegie Mellon University) · Sivaraman Balakrishnan (CMU) · Zachary Lipton (Carnegie Mellon University)

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction
Aviral Kumar (UC Berkeley) · Abhishek Gupta (University of California, Berkeley) · Sergey Levine (UC Berkeley)

Reward Propagation Using Graph Convolutional Networks
Martin Klissarov (Mila/McGill University) · Doina Precup (McGill University / Mila / DeepMind Montreal)

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
Alekh Agarwal (Microsoft Research) · Sham Kakade (University of Washington & Microsoft Research) · Akshay Krishnamurthy (Microsoft) · Wen Sun (Microsoft Research NYC)

Neurosymbolic Reinforcement Learning with Formally Verified Exploration
Greg Anderson (University of Texas at Austin) · Abhinav Verma (Rice University) · Isil Dillig (UT Austin) · Swarat Chaudhuri (The University of Texas at Austin)

Privacy Amplification via Random Check-Ins
Borja Balle (Amazon) · Peter Kairouz (Google) · Brendan McMahan (Google) · Om Thakkar (Google) · Abhradeep Thakurta (Google)

Learning Invariances in Neural Networks from Training Data
Gregory Benton (New York University) · Marc Finzi (New York University) · Pavel Izmailov (New York University) · Andrew Gordon Wilson (New York University)

Geometric All-way Boolean Tensor Decomposition
Changlin Wan (Department of Electrical and Computer Engineering, Purdue University) · Wennan Chang (Department of Electrical and Computer Engineering, Purdue University) · Tong Zhao (Amazon) · Sha Cao (Indiana University) · Chi Zhang (Indiana University School of Medicine)

Optimizing Neural Networks via Koopman Operator Theory
Akshunna Dogra (Harvard University) · William T Redman (UC Santa Barbara)

Better Full-Matrix Regret via Parameter-Free Online Learning
Ashok Cutkosky (Google Research)

Generalized Hindsight for Reinforcement Learning
Alexander Li (UC Berkeley) · Lerrel Pinto (New York University) · Pieter Abbeel (UC Berkeley & covariant.ai)

Learning to Select Best Forecast Tasks for Clinical Outcome Prediction
Yuan Xue (Google) · Nan Du (Google Brain) · Anne Mottram (Google Health) · Martin Seneviratne (Google Health) · Andrew Dai (Google Brain)

Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes
Hao Chen (University of Wisconsin-Madison) · Lili Zheng (University of Wisconsin-Madison) · Raed AL Kontar (University of Michigan) · Garvesh Raskutti (University of Wisconsin-Madison)

Learning Some Popular Gaussian Graphical Models without Condition Number Bounds
Jonathan Kelner (MIT) · Frederic Koehler (MIT) · Raghu Meka (UCLA) · Ankur Moitra (MIT)

X-CAL: Explicit Calibration for Survival Analysis
Xintian Han (New York University) · Mark Goldstein (New York University) · Aahlad Manas Puli (NYU) · Adler Perotte (Columbia University) · Rajesh Ranganath (New York University)

Exemplar Guided Active Learning
Jason Hartford (University of British Columbia) · Kevin Leyton-Brown (University of British Columbia) · Hadas Raviv (AI21 Labs) · Dan Padnos (AI21 Labs) · Shahar Lev (AI21 Labs) · Barak Lenz (AI21 Labs)

Smoothed Geometry for Robust Attribution
Zifan Wang (Carnegie Mellon University) · Haofan Wang (Carnegie Mellon University) · Shakul Ramkumar (Carnegie Mellon University) · Piotr Mardziel (Carnegie Mellon University) · Matt Fredrikson (CMU) · Anupam Datta (Carnegie Mellon University)

Barking up the right tree: an approach to search over molecule synthesis DAGs
John Bradshaw (University of Cambridge/MPI IS Tübingen) · Brooks Paige (University College London) · Matt Kusner (University College London) · Marwin Segler (BenevolentAI) · José Miguel Hernández-Lobato (University of Cambridge)

Subgraph Neural Networks
Emily Alsentzer (MIT) · Samuel Finlayson (Harvard Medical School) · Michelle Li (Harvard Medical School) · Marinka Zitnik (Harvard University)

Interior Point Solving for LP-based prediction+optimisation
Jayanta Mandi (Vrije Universiteit Brussel) · Tias Guns (Vrije Universiteit Brussel)

Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering
Meng Liu (Purdue University) · David Gleich (Purdue University)

Multi-Plane Program Induction with 3D Box Priors
Yikai Li (Shanghai Jiao Tong University) · Jiayuan Mao (MIT) · Xiuming Zhang (MIT) · Bill Freeman (MIT/Google) · Josh Tenenbaum (MIT) · Noah Snavely (Cornell University and Google AI) · Jiajun Wu (Google)

Identifying Causal Effect Inference Failure with Uncertainty-Aware Models
Andrew Jesson (University of Oxford) · Sören Mindermann (University of Oxford) · Uri Shalit (Technion) · Yarin Gal (University of Oxford)

Sequential Bayesian Experimental Design with Variable Cost Structure
Sue Zheng (MIT) · David Hayden (Massachusetts Institute of Technology) · Jason Pacheco (Univ. of Arizona) · John W Fisher III (MIT)

A Fair Classifier Using Kernel Density Estimation
Jaewoong Cho (KAIST) · Gyeongjo Hwang (KAIST) · Changho Suh (KAIST)

On Understanding the Global Landscape of Generative Adversarial Nets
Ruoyu Sun (University of Illinois at Urbana-Champaign) · Tiantian Fang (University of Illinois Urbana-Champaign) · Alexander Schwing (University of Illinois at Urbana-Champaign)

Sparsity, Conjugacy, and Mean Field Variational Inference
Jeffrey Spence (Stanford University)

Large-Scale Methods for Distributionally Robust Optimization
Daniel Levy (Stanford University) · Yair Carmon (Stanford University) · John Duchi (Stanford) · Aaron Sidford (Stanford)

The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
Adam Smith (Boston University) · Shuang Song (Google) · Abhradeep Thakurta (Google)

Generalized Control Functions for Causal Effect Estimation from IVs
Aahlad Manas Puli (NYU) · Rajesh Ranganath (New York University)

Finite-Time Analysis for Double Q-learning
Huaqing Xiong (Ohio State University) · Lin Zhao (National University of Singapore) · Yingbin Liang (The Ohio State University) · Wei Zhang (Southern University of Science and Technology)

Understanding Gradient Clipping in Private SGD: A Geometric Perspective
Xiangyi Chen (University of Minnesota) · Steven Wu (Carnegie Mellon University) · Mingyi Hong (University of Minnesota)

Learning Strategic Network Emergence Games
Rakshit Trivedi (Georgia Institute of Technology) · Hongyuan Zha (Georgia Tech)

Multi-ON: Benchmarking Semantic Map Memory using Multi-Object Navigation
Saim Wani (Indian Institute of Technology Kanpur) · Shivansh Patel (Indian Institute of Technology Kanpur) · Unnat Jain (University of Illinois at Urbana Champaign) · Angel Chang (Simon Fraser University) · Manolis Savva (Simon Fraser University)

NeuralMeshFlow: 3D Manifold Mesh Generation via Diffeomorphic Flows
Kunal Gupta (University of California San Diego) · Manmohan Chandraker (UC San Diego)

Subgroup-based Rank-1 Lattice Quasi-Monte Carlo
Yueming LYU (University of Technology Sydney) · Yuan Yuan (MIT) · Ivor Tsang (University of Technology, Sydney)

Sparse Weight Activation Training
Md Aamir Raihan (University of British Columbia) · Tor Aamodt (University of British Columbia)

Certifying Confidence via Randomized Smoothing
Aounon Kumar (University of Maryland, College Park) · Alexander Levine (University of Maryland, College Park) · Soheil Feizi (University of Maryland) · Tom Goldstein (University of Maryland)

Program Synthesis with Pragmatic Communication
Yewen Pu (MIT) · Kevin Ellis (MIT) · Marta Kryven (Massachusetts Institute of Technology) · Josh Tenenbaum (MIT) · Armando Solar-Lezama (MIT)

Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards
Kyungjae Lee (Seoul National University) · Hongjun Yang (Ulsan National Institute of Science and Technology) · Sungbin Lim (UNIST) · Songhwai Oh (Seoul National University)

Adversarial robustness via robust low rank representations
Pranjal Awasthi (Rutgers University/Google) · Himanshu Jain (Google) · Ankit Singh Rawat (Google Research) · Aravindan Vijayaraghavan (Northwestern University)

Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks
Wei-An Lin (Adobe) · Chun Pong Lau (University of Maryland, College Park) · Alexander Levine (University of Maryland, College Park) · Rama Chellappa (University of Maryland College Park) · Soheil Feizi (University of Maryland)

Meta-Gradient Reinforcement Learning with an Objective Discovered Online
Zhongwen Xu (DeepMind) · Hado van Hasselt (DeepMind) · Matteo Hessel (Google DeepMind) · Junhyuk Oh (DeepMind) · Satinder Singh (DeepMind) · David Silver (DeepMind)

TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search
Tarun Gogineni (University of Michigan) · Ziping Xu (University of Michigan) · Exequiel Punzalan (University of Michigan) · Runxuan Jiang (University of Michigan) · Joshua Kammeraad (University of Michigan) · Ambuj Tewari (University of Michigan) · Paul Zimmerman (University of Michigan)

Fair Hierarchical Clustering
Sara Ahmadian (Google Research) · Alessandro Epasto (Google) · Marina Knittel (University of Maryland, College Park) · Ravi Kumar (Google) · Mohammad Mahdian (Google Research) · Benjamin Moseley (Carnegie Mellon University) · Philip Pham (Google) · Sergei Vassilvitskii (Google) · Yuyan Wang (Carnegie Mellon University)

Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps
Chaozheng Wu (South China University of Technology) · Jian Chen (South China University of Technology) · Qiaoyu Cao (South China University of Technology ) · Jianchi Zhang (SCUT) · Yunxin Tai (SCUT) · Lin Sun (Samsung, Stanford, HKUST) · Kui Jia (South China University of Technology)

Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms
Xiangyi Chen (University of Minnesota) · Tiancong Chen (University of Minnesota) · Haoran Sun (University of Minnesota) · Steven Wu (Carnegie Mellon University) · Mingyi Hong (University of Minnesota)

Sparse and Continuous Attention Mechanisms
André Martins (Instituto de Telecomunicacoes (NIF: 502 854 200)) · Marcos Treviso (Instituto de Telecomunicacoes) · António Farinhas (Instituto de Telecomunicações, Instituto Superior Técnico) · Vlad Niculae (Instituto de Telecomunicações) · Mario Figueiredo (University of Lisbon) · Pedro Aguiar (Instituto Superior Técnico)

Decodable Information Bottleneck for Representation Learning
Yann Dubois (Facebook AI Research) · Douwe Kiela (Facebook AI Research) · David Schwab (Facebook AI Research) · Ramakrishna Vedantam (Facebook AI Research)

PyGlove: Symbolic Programming for Automated Machine Learning
Daiyi Peng (Google) · Xuanyi Dong (University of Technology Sydney) · Esteban Real (Google Brain) · Mingxing Tan (Google Brain) · Yifeng Lu () · Gabriel Bender (Google Brain) · Hanxiao Liu (Google Brain) · Adam Kraft (Google) · Chen Liang (Google Brain) · Quoc V Le (Google)

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
Jiong Zhu (University of Michigan) · Yujun Yan (University of Michigan) · Lingxiao Zhao (Carnegie Mellon University) · Mark Heimann (University of Michigan) · Leman Akoglu (CMU) · Danai Koutra (U Michigan)

Learning by Minimizing the Sum of Ranked Range
Shu Hu (University at Buffalo, State University of New York) · Yiming Ying (State University of New York at Albany) · xin wang (CuraCloud) · Siwei Lyu (University at Albany)

From Boltzmann Machines to Neural Networks and Back Again
Surbhi Goel (The University of Texas at Austin) · Adam Klivans (UT Austin) · Frederic Koehler (MIT)

BayReL: Bayesian Relational Learning for Multi-omics Data Integration
Ehsan Hajiramezanali (Texas A&M University) · Arman Hasanzadeh (Texas A&M University) · Nick Duffield (Texas A&M University) · Krishna Narayanan (Texas A&M University) · Xiaoning Qian (Texas A&M)

MetaPoison: Practical General-purpose Clean-label Data Poisoning
W. Ronny Huang (EY) · Jonas Geiping (University of Siegen) · Liam Fowl (University of Maryland) · Gavin Taylor (US Naval Academy) · Tom Goldstein (University of Maryland)

Learning Skillful Resets: Acquisition of Behavior via Reset-Free Games
Kelvin Xu (UC Berkeley) · Siddharth Verma (UC Berkeley) · Chelsea Finn (Stanford) · Sergey Levine (UC Berkeley)

Sparse Symplectically Integrated Neural Networks
Daniel DiPietro (Dartmouth College) · Shiying Xiong (Dartmouth College) · Bo Zhu (Dartmouth College)

Efficient Learning of Discrete Graphical Models
Marc Vuffray (Los Alamos National Laboratory) · Sidhant Misra (Los Alamos National Laboratory) · Andrey Lokhov (LANL)

Adaptive Gradient Methods Converge Under Heavy-tailed Noise
Jingzhao Zhang (MIT) · Sai Praneeth Karimireddy (EPFL) · Andreas Veit (Google) · Seungyeon Kim (Google Research) · Sashank Reddi (Google) · Sanjiv Kumar (Google Research) · Suvrit Sra (MIT)

Distributionally Robust Federated Averaging
Yuyang Deng (Penn State) · Mohammad Mahdi Kamani (Pennsylvania State University) · Mehrdad Mahdavi (Pennsylvania State University)

Unsupervised Learning of Dense Visual Representations
Pedro O. Pinheiro (Element AI) · Amjad Almahairi (Element AI) · Ryan Benmalek (Cornell University) · Florian Golemo (MILA / ElementAI) · Aaron Courville (U. Montreal)

Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning​
Shreyas Fadnavis (Indiana University Bloomington) · Joshua Batson (CZ Biohub) · Eleftherios Garyfallidis (Indiana University)

Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters
Sulin Liu (Princeton) · Xingyuan Sun (Princeton University) · Peter J Ramadge (Princeton) · Ryan Adams (Princeton University)

A Benchmark for Systematic Generalization in Grounded Language Understanding
Laura Ruis (University of Amsterdam) · Jacob Andreas (MIT) · Marco Baroni (Facebook Artificial Intelligence Research) · Diane Bouchacourt (Facebook AI) · Brenden Lake (New York University)

Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy
Edward Moroshko (Technion) · Suriya Gunasekar (Microsoft Research Redmond) · Blake Woodworth (TTIC) · Jason Lee (Princeton University) · Nati Srebro (TTI-Chicago) · Daniel Soudry (Technion)

Marginal Utility for Planning in Continuous or Large Discrete Action Spaces
Zaheen F Ahmad (University of Alberta) · Levi Lelis (Universidade Federal de Viçosa) · Michael Bowling (University of Alberta / DeepMind)

A Biologically Plausible Neural Network for Slow Feature Analysis
David Lipshutz (Flatiron Institute) · Charles Windolf (Flatiron Institute) · Siavash Golkar (Flatiron Institute) · Dmitri Chklovskii (Flatiron Institute, Simons Foundation)

A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses
Ambar Pal (Johns Hopkins University) · Rene Vidal (Johns Hopkins University, USA)

Learning Linear Programs from Optimal Decisions
Yingcong Tan (Concordia University) · Daria Terekhov (Concordia University) · Andrew Delong (Concordia University)

AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows
Hadi Mohaghegh Dolatabadi (University of Melbourne) · Sarah Erfani (University of Melbourne) · Christopher Leckie (University of Melbourne)

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
LEI BAI (UNSW, Sydney) · Lina Yao (University of New South Wales) · Can Li (University of New South Wales) · Xianzhi Wang (University of Technology Sydney) · Can Wang (Griffith University)

Non-Stochastic Control with Bandit Feedback
Paula Gradu (Princeton University, Google AI Princeton) · John Hallman (Princeton University) · Elad Hazan (Princeton University)

Assisted Learning: A Framework for Multiple Organizations Learning
Xun Xian (University of Minnesota) · Xinran Wang (University of Minnesota) · Jie Ding (University of Minnesota) · Reza Ghanadan (Google)

Uncertainty Aware Semi-Supervised Learning on Graph Data
Xujiang Zhao (The University of Texas at Dallas) · Feng Chen (UT Dallas) · Shu Hu (University at Buffalo, State University of New York) · Jin-Hee Cho (Virginia Tech)

A smoothed GDA algorithm for the nonconvex-concave min-max problem with an $\mathcal{O}\left(1/\epsilon^2\right)$ iteration complexity
Jiawei Zhang (The Chinese University of Hong Kong, Shenzhen) · Peijun Xiao (University of Illinois at Urbana-Champaign (UIUC)) · Ruoyu Sun (University of Illinois at Urbana-Champaign) · Zhiquan Luo (The Chinese University of Hong Kong, Shenzhen and Shenzhen Research Institute of Big Data)

Optimizing Mode Connectivity via Neuron Alignment
Norman Tatro (Rensselaer Polytechnic Institute) · Pin-Yu Chen (IBM Research AI) · Payel Das (IBM Research) · Igor Melnyk (IBM Research) · Prasanna Sattigeri (IBM Research) · Rongjie Lai (Rensselaer Polytechnic Institute)

Transfer Learning via $\ell_1$ Regularization
Masaaki Takada (Toshiba Corporation) · Hironori Fujisawa (The Institute of Statistical Mathematics)

DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
Dennis Wei (IBM Research) · Tian Gao (IBM Research AI) · yue yu (Lehigh University)

AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning
Hao Zhang (Carnegie Mellon University, Petuum Inc.) · Yuan Li (Duke University) · Zhijie Deng (Tsinghua University) · Xiaodan Liang (Sun Yat-sen University) · Lawrence Carin (Duke University) · Eric Xing (Petuum Inc. / Carnegie Mellon University)

Input-Aware Dynamic Backdoor Attack
Tuan Anh Nguyen (VinAI Research/Hanoi University of Science and Technology) · Anh Tran (VinAI Research)

Partially View-aligned Clustering
Zhenyu Huang (Sichuan University) · Peng Hu (Institute for Infocomm Research, A*STAR) · Joey Tianyi Zhou (IHPC, A*STAR) · Jiancheng Lv (Machine Intelligence Laboratory College of Computer Science, Sichuan University) · Xi Peng (Institute for Infocomm, Research Agency for Science, Technology and Research (A*STAR) Singapore)

Weston-Watkins Hinge Loss and Ordered Partitions
Yutong Wang (University of Michigan) · Clayton Scott (University of Michigan)

Succinct and Robust Multi-Agent Communication With Temporal Message Control
Sai Qian Zhang (Harvard University) · Qi Zhang (Amazon) · Jieyu Lin (University of Toronto)

Learning Utilities and Equilibria in Non-Truthful Auctions
Hu Fu (University of British Columbia) · Tao Lin (Peking University)

Personalized Federated Learning with Moreau Envelopes
Canh T. Dinh (The University of Sydney) · Nguyen H. Tran (The University of Sydney) · Tuan Dung Nguyen (The University of Melbourne)

How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods
Jeya Vikranth Jeyakumar (University of California, Los Angeles) · Joseph Noor (University of California, Los Angeles) · Yu-Hsi Cheng (UCLA) · Luis Garcia (University of California, Los Angeles) · Mani Srivastava (UCLA)

Myersonian Regression
Allen Liu (MIT) · Renato Leme (Google Research) · Jon Schneider (Google Research)

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Cong Zhang (Nanyang Technological University) · Wen Song (Institute of Marine Scinece and Technology, Shandong University) · Zhiguang Cao (National University of Singapore) · Jie Zhang (Nanyang Technological University) · Puay Siew Tan (SIMTECH) · Xu Chi (Singapore Institute of Manufacturing Technology, A-Star)

Reciprocal Adversarial Learning via Characteristic Functions
Shengxi Li (Imperial College London) · Zeyang Yu (Imperial College London) · Min Xiang (Imperial College London) · Danilo P Mandic (Imperial College London)

Reward-rational (implicit) choice: A unifying formalism for reward learning
Hong Jun Jeon (Stanford University) · Smitha Milli (UC Berkeley) · Anca Dragan (UC Berkeley)

Statistical-Query Lower Bounds via Functional Gradients
Surbhi Goel (The University of Texas at Austin) · Aravind Gollakota (University of Texas at Austin) · Adam Klivans (UT Austin)

Sampling-Decomposable Generative Adversarial Recommender
Binbin Jin (University of Science and Technology of China) · Defu Lian (University of Science and Technology of China) · Zheng Liu (Microsoft) · Qi Liu (" University of Science and Technology of China, China") · Jianhui Ma (University of Science and Technology of China) · Xing Xie (Microsoft Research Asia) · Enhong Chen (University of Science and Technology of China)

Boundary thickness and robustness in learning models
Yaoqing Yang (UC Berkeley) · Rajiv Khanna (University of California, Berkeley) · Yaodong Yu (University of California, Berkeley) · Amir Gholami (University of California, Berkeley) · Kurt Keutzer (EECS, UC Berkeley) · Joseph Gonzalez (UC Berkeley) · Kannan Ramchandran (UC Berkeley) · Michael W Mahoney (UC Berkeley)

Biologically Inspired Mechanisms for Adversarial Robustness
Manish Vuyyuru Reddy (Harvard) · Andrzej Banburski (MIT) · Nishka Pant (MIT) · Tomaso Poggio (MIT)

Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition
Lin Chen (University of California, Berkeley) · Qian Yu (University of Southern California) · Hannah Lawrence (Flatiron Institute) · Amin Karbasi (Yale)

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
David Bieber (Google Brain) · Charles Sutton (Google) · Hugo Larochelle (Google Brain) · Daniel Tarlow (Google Brain)

Towards Interpretable Natural Language Understanding with Explanations as Latent Variables
Wangchunshu Zhou (Beihang University) · Jinyi Hu (Tsinghua University) · Hanlin Zhang (South China University of Technology) · Xiaodan Liang (Sun Yat-sen University) · Maosong Sun (Tsinghua University) · Chenyan Xiong (Microsoft Research AI) · Jian Tang (Mila)

Randomized tests for high-dimensional regression: A more efficient and powerful solution
Yue Li (Carnegie Mellon University) · Ilmun Kim (CMU) · Yuting Wei (Carnegie Mellon University)

Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization
Chong You (University of California, Berkeley) · Zhihui Zhu (Johns Hopkins University) · Qing Qu (New York University) · Yi Ma (UC Berkeley)

Adversarial Contrastive Learning: Harvesting More Robustness from Unsupervised Pre-Training
Ziyu Jiang (Texas A&M University) · Tianlong Chen (Unversity of Texas at Austin) · Ting Chen (Google) · Zhangyang Wang (University of Texas at Austin)

Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?
Qiwen Cui (Peking University) · Lin Yang (UCLA)

Online Convex Optimization Over Erdos-Renyi Random Networks
Jinlong Lei (Tongji University) · Peng Yi (Tongji University) · Yiguang Hong (Academy of Mathematics and Systems Science, Chinese Academy of Sciences) · Jie Chen (Beijing Institute of Technology) · Guodong Shi (University of Sydney)

Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning
Arnab Bhattacharyya (National University of Singapore) · Sutanu Gayen (National University of SIngapore) · Kuldeep S Meel (National University of Singapore) · N. V. Vinodchandran (University of Nebraska)

Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions
Soumya Banerjee (Minnesota State University, Mankato)

Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations
Rohan Paleja (Georgia Institute of Technology) · Andrew Silva (Georgia Institute of Technology) · Letian Chen (Georgia Institute of Technology) · Matthew Gombolay (Georgia Institute of Technology)

Towards Better Generalization of Adaptive Gradient Methods
Yingxue Zhou (University of Minnesota) · Belhal Karimi (Baidu Research) · Jinxing Yu (Baidu Research) · Zhiqiang Xu (Baidu Inc.) · Ping Li (Baidu Research USA)

Asymptotically Optimal Exact Minibatch Metropolis-Hastings
Ruqi Zhang (Cornell University) · A. Feder Cooper (Cornell University) · Christopher De Sa (Cornell)

Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks
Zhou Fan (Yale University) · Zhichao Wang (UC San Diego)

Instance-based Generalization in Reinforcement Learning
Martin Bertran (Duke University) · Natalia L Martinez (Duke University) · Mariano Phielipp (Intel AI Labs) · Guillermo Sapiro (Duke University)

Community detection using fast low-cardinality semidefinite programming

Po-Wei Wang (CMU) · J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

Fair Performance Metric Elicitation
Gaurush Hiranandani (University of Illinois at Urbana-Champaign) · Harikrishna Narasimhan (Google Research) · Oluwasanmi Koyejo (UIUC)

Neural Networks with Recurrent Generative Feedback
Yujia Huang (Caltech) · James Gornet (California Institute of Technology) · Sihui Dai (California Institute of Technology) · Zhiding Yu (NVIDIA) · Tan Nguyen (Rice University/UCLA) · Doris Tsao (Caltech) · Anima Anandkumar (NVIDIA / Caltech)

Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function
Lingkai Kong (Georgia Institute of Technology) · Molei Tao (Georgia Institute of Technology)

Calibrated Reliable Regression using Maximum Mean Discrepancy
Peng Cui (Tsinghua University) · Wenbo Hu (Tsinghua University) · Jun Zhu (Tsinghua University)

Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits
Shinji Ito (NEC Corporation) · Shuichi Hirahara (National Institute of Informatics) · Tasuku Soma (University of Tokyo) · Yuichi Yoshida (National Institute of Informatics and Preferred Infrastructure, Inc.)

Deep Direct Likelihood Knockoffs
Mukund Sudarshan (NYU) · Wesley Tansey (Columbia University) · Rajesh Ranganath (New York University)

AViD Dataset: Anonymized Videos from Diverse Countries
Anthony J Piergiovanni (Indiana University) · Michael S Ryoo (Stony Brook University)

How to Characterize The Landscape of Overparameterized Convolutional Neural Networks
Weizhong Zhang (Zhejiang University) · Yihong Gu (Princeton University) · Cong Fang (Peking University) · Jason Lee (Princeton University) · Tong Zhang (Tencent AI Lab)

Practical No-box Adversarial Attacks against DNNs
Qizhang Li (ByteDance AI Lab) · Yiwen Guo (ByteDance AI Lab) · Hao Chen (UC Davis)

Preference-based Reinforcement Learning with Finite-Time Guarantees
Yichong Xu (Carnegie Mellon University) · Ruosong Wang (Carnegie Mellon University) · Lin Yang (UCLA) · Aarti Singh (CMU) · Artur Dubrawski (Carnegie Mellon University)

Consistency Regularization for Certified Robustness of Smoothed Classifiers
Jongheon Jeong (KAIST) · Jinwoo Shin (KAIST)

Learning Compositional Rules via Neural Program Synthesis
Maxwell Nye (MIT) · Armando Solar-Lezama (MIT) · Josh Tenenbaum (MIT) · Brenden Lake (New York University)

Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
Hongyu Ren (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

Analytical Probability Distributions and EM-Learning for Deep Generative Networks
Randall Balestriero (Rice University) · Sebastien PARIS (University of Toulon) · Richard Baraniuk (Rice University)

Predicting Training Time Without Training
Luca Zancato (University of Padova) · Alessandro Achille (Amazon Web Services) · Avinash Ravichandran (AWS) · Rahul Bhotika (Amazon) · Stefano Soatto (UCLA)

Stage-wise Conservative Linear Bandits
Ahmadreza Moradipari (University of California, Santa Barbara) · Christos Thrampoulidis (UCSB) · Mahnoosh Alizadeh (University of California Santa Barbara)

Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes
Salman Habib (New Jersey Institute of Tech) · Allison Beemer (New Jersey Institute of Technology) · Joerg Kliewer (New Jersey Institute of Technology)

Multiparameter Persistence Image for Topological Machine Learning
Mathieu Carrière (Inria Sophia Antipolis) · Andrew Blumberg (University of Texas)

A Tight Lower Bound and Efficient Reduction for Swap Regret
Shinji Ito (NEC Corporation)

Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
Alireza Fallah (MIT) · Aryan Mokhtari (UT Austin) · Asuman Ozdaglar (Massachusetts Institute of Technology)

What shapes feature representations? Exploring datasets, architectures, and training
Katherine Hermann (Stanford University) · Andrew Lampinen (Stanford University)

Implicit Regularization and Convergence for Weight Normalization
Xiaoxia Wu (The University of Texas at Austin) · Edgar Dobriban (University of Pennsylvania) · Tongzheng Ren (UT Austin) · Shanshan Wu (University of Texas at Austin) · Zhiyuan Li (Princeton University) · Suriya Gunasekar (Microsoft Research Redmond) · Rachel Ward (UT Austin) · Qiang Liu (Dartmouth College)

Causal Discovery in Physical Systems from Videos
Yunzhu Li (MIT) · Antonio Torralba (Massachusetts Institute of Technology) · Anima Anandkumar (NVIDIA / Caltech) · Dieter Fox (NVIDIA) · Animesh Garg (Univ. of Toronto, Vector Institute, Nvidia)

Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
Nathan Inkawhich (Duke University) · Kevin Liang (Duke University) · Binghui Wang (Duke University) · Matthew J Inkawhich (Duke University) · Lawrence Carin (Duke University) · Yiran Chen (Duke University)

Identifying learning rules from neural network observables
Aran Nayebi (Stanford University) · Sanjana Srivastava (Stanford University) · Surya Ganguli (Stanford) · Daniel Yamins (Stanford University)

Understanding and Exploring the Network with Stochastic Architectures
Zhijie Deng (Tsinghua University) · Yinpeng Dong (Tsinghua University) · Shifeng Zhang (Department of Computer Science and Technology, Tsinghua University) · Jun Zhu (Tsinghua University)

Multi-label Contrastive Predictive Coding
Jiaming Song (Stanford University) · Stefano Ermon (Stanford)

Gradient-EM Bayesian Meta-Learning
Yayi Zou (Didi Research America) · Xiaoqi Lu (Columbia University)

BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
Xinyue Chen (NYU Shanghai) · Zijian Zhou (NYU Shanghai) · Zheng Wang (NYU Shanghai) · Che Wang (New York University) · Yanqiu Wu (New York University) · Keith Ross (NYU Shanghai)

Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability
Sitan Chen (MIT) · Frederic Koehler (MIT) · Ankur Moitra (MIT) · Morris Yau (UC Berkeley)

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
Mengdi Xu (Carnegie Mellon University) · Wenhao Ding (Carnegie Mellon University) · Jiacheng Zhu (Carnegie Mellon University) · ZUXIN LIU (Carnegie Mellon University) · Baiming Chen (Tsinghua University) · Ding Zhao (Carnegie Mellon University)

Distributed Newton Can Communicate Less and Resist Byzantine Workers
Avishek Ghosh (University of California, Berkeley) · Raj Kumar Maity (University of Massachusetts Amherst) · Arya Mazumdar (University of Massachusetts Amherst)

Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks
Yimeng Min (MILA) · Frederik Wenkel (Mila, Université de Montréal) · Guy Wolf (Université de Motréal; Mila)

Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
Yulin Wang (Tsinghua University) · Kangchen Lv (Tsinghua University) · Rui Huang (Tsinghua University) · Shiji Song (Department of Automation, Tsinghua University) · Le Yang (Tsinghua University) · Gao Huang (Tsinghua)

Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
Ben Letham (Facebook) · Roberto Calandra (Facebook AI Research) · Akshara Rai (Facebook) · Eytan Bakshy (Facebook)

Learning Structured Distributions From Untrusted Batches: Faster and Simpler
Sitan Chen (MIT) · Jerry Li (Microsoft) · Ankur Moitra (MIT)

On Adaptive Attacks to Adversarial Example Defenses
Florian Tramer (Stanford University) · Nicholas Carlini (Google) · Wieland Brendel (University of Tübingen) · Aleksander Madry (MIT)

Universal guarantees for decision tree induction via a higher-order splitting criterion
Guy Blanc (Stanford University) · Neha Gupta (Stanford University) · Jane Lange (MIT) · Li-Yang Tan (Stanford University)

OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling
Viet Huynh (Monash University) · He Zhao (Monash University) · Dinh Phung (Monash University)

Nonasymptotic Guarantees for Low-Rank Matrix Recovery with Generative Priors
Jorio Cocola (Northeastern University) · Paul Hand (Northeastern University) · Vlad Voroninski (Helm.ai)

Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
Zihang Dai (Carnegie Mellon University) · Guokun Lai (Carnegie Mellon University) · Yiming Yang (CMU) · Quoc V Le (Google)

On Reward-Free Reinforcement Learning with Linear Function Approximation
Ruosong Wang (Carnegie Mellon University) · Simon Du (Institute for Advanced Study) · Lin Yang (UCLA) · Russ Salakhutdinov (Carnegie Mellon University)

Generalized Leverage Score Sampling for Neural Networks
zheng Yu (Princeton University) · Ruoqi Shen (University of Washington) · Zhao Song (IAS/Princeton) · Jason Lee (Princeton University) · Mengdi Wang (Princeton University)

Causal Estimation with Functional Confounders
Aahlad Manas Puli (NYU) · Adler Perotte (Columbia University) · Rajesh Ranganath (New York University)

Estimating decision tree learnability with polylogarithmic sample complexity
Guy Blanc (Stanford University) · Neha Gupta (Stanford University) · Jane Lange (MIT) · Li-Yang Tan (Stanford University)

AvE: Assistance via Empowerment
Yuqing Du (UC Berkeley) · Stas Tiomkin (EECS Department, University of California, Berkeley) · Emre Kiciman (Microsoft Research) · Daniel Polani (University of Hertfordshire) · Pieter Abbeel (UC Berkeley & covariant.ai) · Anca Dragan (UC Berkeley)

Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds
Nicholas Harvey (University of British Columbia) · Christopher Liaw (University of British Columbia) · Tasuku Soma (University of Tokyo)

Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge
Chaoyang He (University of Southern California) · Murali Annavaram (University of Southern California) · Salman Avestimehr (University of Southern California)

Tensor Completion Made Practical
Allen Liu (MIT) · Ankur Moitra (MIT)

Meta-Learning Requires Meta-Augmentation
Janarthanan Rajendran (University of Michigan) · Alexander Irpan (Google Brain) · Eric Jang (Google Brain)

Online Linear Optimization with Many Hints
Aditya Bhaskara (University of Utah) · Ashok Cutkosky (Google Research) · Ravi Kumar (Google) · Manish Purohit (Google)

Learnability with Indirect Supervision Signals
Kaifu Wang (University of Pennsylvania) · Qiang Ning (Allen Institute for AI) · Dan Roth (UPenn)

A Bayesian Perspective on Training Speed and Model Selection
Clare Lyle (University of Oxford) · Lisa Schut (University of Oxford) · Robin Ru (Oxford University) · Yarin Gal (University of Oxford) · Mark van der Wilk (Imperial College)

An Unsupervised Information-Theoretic Perceptual Quality Metric
Sangnie Bhardwaj (Google LLC) · Ian Fischer (Google) · Johannes Ballé (Google) · Troy Chinen (Google)

Uncertainty Quantification for Inferring Hawkes Networks
Haoyun Wang (Georgia Tech) · Liyan Xie (Georgia Institute of Technology) · Alex Cuozzo (Duke University) · Simon Mak (Duke University) · Yao Xie (Georgia Institute of Technology)

Bayesian Attention Modules
Xinjie Fan (UT Austin) · Shujian Zhang (UT Austin) · Bo Chen (Xidian University) · Mingyuan Zhou (University of Texas at Austin)

Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
Disi Ji (UC, Irvine) · Padhraic Smyth (University of California, Irvine) · Mark Steyvers (UC Irvine)

A Dynamical Central Limit Theorem for Two-Layer Neural Networks
Zhengdao Chen (New York University) · Grant Rotskoff (New York University) · Joan Bruna (NYU) · Eric Vanden-Eijnden (New York University)

Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
Jianyu Wang (Carnegie Mellon University) · Qinghua Liu (Princeton University) · Hao Liang (Carnegie Mellon University) · Gauri Joshi (Carnegie Mellon University) · H. Vincent Poor (Princeton University)

Near-Optimal Reinforcement Learning with Self-Play
Yu Bai (Salesforce Research) · Chi Jin (Princeton University) · Tiancheng Yu (MIT )

Exact expressions for double descent and implicit regularization via surrogate random design
Michal Derezinski (UC Berkeley) · Feynman T Liang (Berkeley) · Michael W Mahoney (UC Berkeley)

Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs
Agniva Chowdhury (Purdue University) · Palma London (Caltech) · Haim Avron (Tel Aviv University) · Petros Drineas (Purdue University)

Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient
Ankit Pensia (University of Wisconsin-Madison) · Shashank Rajput (University of Wisconsin - Madison) · Alliot Nagle (UW-Madison) · Harit Vishwakarma (University of Wisconsin Madison) · Dimitris Papailiopoulos (University of Wisconsin-Madison)

Maximizing Welfare and Revenue Subject to a Filtering Constraint: An Algorithmic Viewpoint
Aranyak Mehta (Google Research) · Uri Nadav (Google) · Alexandros Psomas (Purdue University) · Aviad Rubinstein (Stanford)

Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels
Yi Zhou (University of Southern California) · Chenglei Wu (Facebook) · Zimo Li (University of Southern California) · Chen Cao (Snap Inc.) · Yuting Ye (Facebook Reality Labs) · Jason Saragih (Facebook) · Hao Li (Pinscreen/University of Southern California/USC ICT) · Yaser Sheikh (Facebook Reality Labs)

Counterfactual Prediction for Bundle Treatment
Hao Zou (Tsinghua University) · Peng Cui (Tsinghua University) · Bo Li (Tsinghua University) · Zheyan Shen (Tsinghua University) · Jianxin Ma (Alibaba Group) · Hongxia Yang (Alibaba Group) · Yue He (Tsinghua University)

Rethinking the Value of Labels for Improving Class-Imbalanced Learning
Yuzhe Yang (MIT) · Zhi Xu (MIT)

On Convergence and Generalization of Dropout Training
Poorya Mianjy (Johns Hopkins University) · Raman Arora (Johns Hopkins University)

Finding All $\epsilon$-Good Arms in Stochastic Bandits
Blake Mason (University of Wisconsin - Madison) · Lalit Jain (University of Washington) · Ardhendu Tripathy (University of Wisconsin - Madison) · Robert Nowak (University of Wisconsion-Madison)

FrugalML: How to use ML Prediction APIs more accurately and cheaply
Lingjiao Chen (University of Wisconsin-Madison) · Matei Zaharia (Stanford and Databricks) · James Zou (Stanford University)

Towards Understanding Hierarchical Learning: Benefits of Neural Representations
Minshuo Chen (Georgia Tech) · Yu Bai (Salesforce Research) · Jason Lee (Princeton University) · Tuo Zhao (Gatech) · Huan Wang (Salesforce Research) · Caiming Xiong (Salesforce) · Richard Socher (Salesforce)

Learning the Linear Quadratic Regulator from Nonlinear Observations
Zakaria Mhammedi (The Australian National University and Data61) · Dylan Foster (MIT) · Max Simchowitz (Berkeley) · Wen Sun (Microsoft Research NYC) · Dipendra Misra (Microsoft) · Akshay Krishnamurthy (Microsoft) · Alexander Rakhlin (MIT) · John Langford (Microsoft Research New York)

Ratio Trace Formulation of Wasserstein Discriminant Analysis
Hexuan Liu (University of Washington) · Yunfeng Cai (Baidu Research) · You-Lin Chen (Department of Statistics, University of Chicago) · Ping Li (Baidu Research USA)

Differentially Private Clustering: Tight Approximation Ratios
Badih Ghazi (Google) · Ravi Kumar (Google) · Pasin Manurangsi (Google)

A Novel Automated Curriculum Strategy to Solve Hard AI Planning Instances
Dieqiao Feng (Cornell University) · Carla Gomes (Cornell University) · Bart Selman (Cornell University)

Robust Multi-Agent Reinforcement Learning with Model Uncertainty
Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · TAO SUN (Amazon.com) · Yunzhe Tao (Amazon Artificial Intelligence) · Sahika Genc (Amazon Artificial Intelligence) · Sunil Mallya (Amazon AWS) · Tamer Basar (University of Illinois at Urbana-Champaign)

An Imitation from Observation Approach to Sim-to-Real Transfer
Siddharth Desai (University of Texas at Austin) · Ishan Durugkar (University of Texas at Austin) · Haresh Karnan (University of Texas at Austin) · Garrett Warnell (US Army Research Laboratory) · Josiah Hanna ( University of Edinburgh) · Peter Stone (The University of Texas at Austin)

Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method
Michal Derezinski (UC Berkeley) · Rajiv Khanna (University of California, Berkeley) · Michael W Mahoney (UC Berkeley)

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
Marc Finzi (New York University) · Ke Alexander Wang (Cornell University) · Andrew Gordon Wilson (New York University)

Making Non-Stochastic Control (Almost) as Easy as Stochastic
Max Simchowitz (Berkeley)

Spectral Kalman filtering: Learning to predict in unknown dynamical systems with long-term memory
Paria Rashidinejad (University of California, Berkeley) · Jiantao Jiao (University of California, Berkeley) · Stuart Russell (UC Berkeley)

Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
Yi Tian (MIT) · Jian Qian (MIT) · Suvrit Sra (MIT)

The Lottery Ticket Hypothesis for the Pre-trained BERT Networks
Tianlong Chen (Unversity of Texas at Austin) · Jonathan Frankle (MIT CSAIL) · Shiyu Chang (MIT-IBM Watson AI Lab) · Sijia Liu (MIT-IBM Watson AI Lab, IBM Research) · Yang Zhang (MIT-IBM Watson AI Lab) · Zhangyang Wang (University of Texas at Austin) · Michael Carbin (MIT)

Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems
Ali Sahin Lale (California Institute of Technology) · Kamyar Azizzadenesheli (Caltech) · Babak Hassibi (Caltech) · Anima Anandkumar (NVIDIA / Caltech)

Precise expressions for random projections: Low-rank approximation and randomized Newton
Michal Derezinski (UC Berkeley) · Feynman T Liang (Berkeley) · Zhenyu Liao (University of California, Berkeley) · Michael W Mahoney (UC Berkeley)

Statistical Optimal Transport posed as Learning Kernel Embedding
Saketha Nath Jagarlapudi (IIT Hyderabad) · Pratik Kumar Jawanpuria (Microsoft)

Sinkhorn Natural Gradient for Generative Models
Zebang Shen (University of Pennsylvania) · Zhenfu Wang (Peking University) · Alejandro Ribeiro (University of Pennsylvania) · Hamed Hassani (UPenn)

Training Stronger Baselines for Learning to Optimize
Tianlong Chen (Unversity of Texas at Austin) · Weiyi Zhang (Shanghai Jiao Tong University) · Zhou Jingyang (University of Science and Technology of China) · Shiyu Chang (MIT-IBM Watson AI Lab) · Sijia Liu (MIT-IBM Watson AI Lab, IBM Research) · Lisa Amini (IBM Research) · Zhangyang Wang (University of Texas at Austin)

Offline Imitation Learning with a Misspecified Simulator
Shengyi Jiang (Nanjing University) · Jingcheng Pang (Nanjing University) · Yang Yu (Nanjing University)

Inference and Estimation in Multi-Layer Models
Parthe Pandit (University of California, Los Angeles) · Mojtaba Sahraee Ardakan (UCLA) · Sundeep Rangan (NYU) · Philip Schniter (The Ohio State University) · Alyson Fletcher (UCLA)

Sinkhorn Barycenter via Functional Gradient Descent
Zebang Shen (University of Pennsylvania) · Zhenfu Wang (Peking University) · Alejandro Ribeiro (University of Pennsylvania) · Hamed Hassani (UPenn)

Pruning neural networks without any data by conserving synaptic flow
Hidenori Tanaka (NTT Research, PHI Lab / Stanford University) · Daniel Kunin (Stanford University) · Daniel Yamins (Stanford University) · Surya Ganguli (Stanford)

Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Shali Jiang (Washington University in St. Louis) · Daniel Jiang (Facebook) · Maximilian Balandat (Facebook) · Brian Karrer (Facebook) · Jacob Gardner (University of Pennsylvania) · Roman Garnett (Washington University in St. Louis)

Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization
Michal Derezinski (UC Berkeley) · Burak Bartan (Stanford University) · Mert Pilanci (Stanford) · Michael W Mahoney (UC Berkeley)

Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward
Guannan Qu (California Institute of Technology) · Yiheng Lin (California Institute of Technology) · Adam Wierman (California Institute of Technology) · Na Li (Harvard University)

Practical automated data augmentation with a reduced search space
Ekin Dogus Cubuk (Google Brain) · Barret Zoph (Google Brain) · Jon Shlens (Google Research) · Quoc V Le (Google)

Learning Augmented Energy Minimization via Speed Scaling
Etienne Bamas (EPFL) · Andreas Maggiori (EPFL) · Lars Rohwedder (EPFL) · Ola Svensson (EPFL)

GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs
Sahil Manchanda (IIT Delhi) · AKASH MITTAL (IIT Delhi) · Anuj Dhawan (Indian Institute of Technology Delhi) · Sourav Medya (Kellogg School of Management, Northwestern University) · Sayan Ranu (IIT Delhi) · Ambuj K Singh (UNIVERSITY OF CALIFORNIA, SANTA BARBARA)

Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
Minh Hoang (Carnegie Mellon University) · Nghia Hoang (MIT-IBM Watson AI Lab, IBM Research) · Hai Pham (Carnegie Mellon University) · David Woodruff (Carnegie Mellon University)

AMQ: Automatic Mixed-precision Quantization Based on Hessian Trace
Zhen Dong (UC Berkeley) · Zhewei Yao (UC Berkeley) · Daiyaan Arfeen (UC Berkeley) · Amir Gholami (University of California, Berkeley) · Michael Mahoney (UC Berkeley) · Kurt Keutzer (EECS, UC Berkeley)

Continual Learning of a Sequence of Mixed Tasks
Zixuan Ke (University of Illionis at Chicago) · Bing Liu (University of Illinois at Chicago) · Xingchang Huang (ETH Zurich)

Pre-Training Graph Neural Networks: A Contrastive Learning Framework with Augmentations
Yuning You (Texas A&M University) · Tianlong Chen (Unversity of Texas at Austin) · Yongduo Sui (University of Science and Technology of China) · Ting Chen (Google) · Zhangyang Wang (University of Texas at Austin) · Yang Shen (Texas A&M University)

Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction
Tong He (UCLA) · John Collomosse (Adobe Research) · Hailin Jin (Adobe) · Stefano Soatto (UCLA)

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Pantelis Elinas (Data61) · Edwin Bonilla (Data61) · Louis C. Tiao (University of Sydney)

Constrained episodic reinforcement learning in concave-convex and knapsack settings
Kianté Brantley (The University of Maryland College Park) · Miro Dudik (Microsoft Research) · Thodoris Lykouris (Microsoft Research NYC) · Sobhan Miryoosefi (Princeton University) · Max Simchowitz (Berkeley) · Aleksandrs Slivkins (Microsoft Research) · Wen Sun (Microsoft Research NYC)

Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
Hongyi Wang (University of Wisconsin-Madison) · Kartik Sreenivasan (University of Wisconsin-Madison) · Shashank Rajput (University of Wisconsin - Madison) · Harit Vishwakarma (University of Wisconsin Madison) · Jy-yong Sohn (KAIST) · Saurabh Agarwal (UW-Madison) · Kangwook Lee (UW Madison) · Dimitris Papailiopoulos (University of Wisconsin-Madison)

Linear-Sample Learning of Low-Rank Distributions
Ayush Jain (UC San Diego) · Alon Orlitsky (University of California, San Diego)

The Complete Lasso Tradeoff Diagram
Hua Wang (Wharton School, University of Pennsylvania) · Yachong Yang (University of Pennsylvania) · Zhiqi Bu (University of Pennsylvania) · Weijie Su (The Wharton School, University of Pennsylvania)

Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks
Hongwei Jin (University of Illinois at Chicago) · Zhan Shi (University of Illinois at Chicago) · Venkata Jaya Shankar Ashish Peruri (University of Illinois at Chicago) · Xinhua Zhang (UIC)

Neural encoding with visual attention
Meenakshi Khosla (Cornell University) · Gia Ngo (Cornell University) · Keith Jamison (Cornell University) · Amy Kuceyeski (Cornell University) · Mert Sabuncu (Cornell)

Explainable Voting
Dominik Peters (Carnegie Mellon University) · Ariel Procaccia (Harvard University) · Alexandros Psomas (Purdue University) · Zixin Zhou (Peking University)

A Computational Separation between Private Learning and Online Learning
Mark Bun (Boston University)

A Catalyst Framework for Minimax Optimization
Junchi Yang (University of Illinois) · Siqi Zhang (University of Illinois at Urbana-Champaign) · Negar Kiyavash (École Polytechnique Fédérale de Lausanne) · Niao He (UIUC)

Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation
Devavrat Shah (Massachusetts Institute of Technology) · Dogyoon Song (Massachusetts Institute of Technology) · Zhi Xu (MIT) · Yuzhe Yang (MIT)

Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding
Zhu Zhang (Zhejiang University) · Zhou Zhao (Zhejiang University) · Zhijie Lin (Zhejiang University) · jieming zhu (Huawei Noah''s Ark Lab) · Xiuqiang He (Huawei Noah's Ark Lab)

A General Method for Robust Learning from Batches
Ayush Jain (UC San Diego) · Alon Orlitsky (University of California, San Diego)

Winning the Lottery with Continuous Sparsification
Pedro Savarese (TTIC) · Hugo Silva (Independent Researcher) · Michael Maire (University of Chicago)

ShiftAddNet: A Hardware-Inspired Deep Network
Haoran You (Rice University) · Xiaohan Chen (University of Texas at Austin) · Yongan Zhang (Rice University) · Chaojian Li (Rice University) · Sicheng Li (Alibaba group) · Zihao Liu (Alibaba Group) · Zhangyang Wang (University of Texas at Austin) · Yingyan Lin (Rice University)

Proximal Mapping for Deep Regularization
mao li (University of Illinois at Chicago) · Yingyi Ma (University of Illinois at Chicago) · Xinhua Zhang (UIC)

Curriculum By Smoothing
Samarth Sinha (University of Toronto, Vector Institute) · Animesh Garg (Univ. of Toronto, Vector Institute, Nvidia) · Hugo Larochelle (Google Brain)

Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
Lin Lan (Xi'an Jiaotong University) · Pinghui Wang (Xi'an Jiaotong University) · Xuefeng Du (Xi'an Jiaotong University) · Kaikai Song (Huawei Noah's Ark Lab) · Jing Tao (Xi'an Jiaotong University) · Xiaohong Guan (Xi'an Jiaotong University)

A Novel Approach for Constrained Optimization in Graphical Models
Sara Rouhani (University of Texas at Dallas) · Tahrima Rahman (UT Dallas) · Vibhav Gogate (UT Dallas)

Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Younggyo Seo (KAIST) · Kimin Lee (UC Berkeley) · Ignasi Clavera Gilaberte (UC Berkeley) · Thanard Kurutach (University of California Berkeley) · Jinwoo Shin (KAIST) · Pieter Abbeel (UC Berkeley & covariant.ai)

Random Reshuffling: Simple Analysis with Vast Improvements
Konstantin Mishchenko (KAUST) · Ahmed Khaled Ragab Bayoumi (Cairo University) · Peter Richtarik (KAUST)

Randomly Collected, Worst Case Data
Justin Y. Chen (MIT) · Gregory Valiant (Stanford University) · Paul Valiant (IAS; Purdue University)

Cooperative Heterogeneous Deep Reinforcement Learning
Han Zheng (UTS) · Pengfei Wei (National University of Singapore) · Jing Jiang (University of Technology Sydney) · Guodong Long (University of Technology Sydney (UTS)) · Qinghua Lu (Data61, CSIRO) · Chengqi Zhang (University of Technology Sydney)

Using noise to probe recurrent neural network structure and prune synapses
Eli Moore (University of California, Davis) · Rishidev Chaudhuri (University of California, Davis)

Understanding Deep Architecture with Reasoning Layer
Xinshi Chen (Georgia Institution of Technology) · Yufei Zhang (University of Oxford) · Christoph Reisinger (University of Oxford) · Le Song (Georgia Institute of Technology)

$O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers
Chulhee Yun (MIT) · Yin-Wen Chang (Google Inc.) · Srinadh Bhojanapalli (Google Research) · Ankit Singh Rawat (Google Research) · Sashank Reddi (Google) · Sanjiv Kumar (Google Research)

Adam with Bandit Sampling for Deep Learning
Rui Liu (University of Michigan, Ann Arbor) · Tianyi Wu (University of Michigan, Ann Arbor) · Barzan Mozafari (University of Michigan)

Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models
Adarsh K Jeewajee (MIT) · Leslie Kaelbling (MIT)

Model Interpretability through the lens of Computational Complexity
Pablo Barceló (PUC Chile & Millenium Instititute for Foundational Research on Data) · Mikaël Monet (Millenium Instititute for Foundational Research on Data) · Jorge Pérez (Universidad de Chile) · Bernardo Subercaseaux (Universidad de Chiel)

Stein Self-Repulsive Dynamics: Benefits From Past Samples
Mao Ye (The University of Texas at Austin) · Tongzheng Ren (UT Austin) · Qiang Liu (UT Austin)

Global Convergence of Natural Primal-Dual Method for Constrained Markov Decision Processes
Dongsheng Ding (University of Southern California) · Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Mihailo Jovanovic (University of Southern California) · Tamer Basar (University of Illinois at Urbana-Champaign)

Continuous Meta-Learning without Tasks
James Harrison (Stanford University) · Apoorva Sharma (Stanford University) · Chelsea Finn (Stanford) · Marco Pavone (Stanford University)

FleXOR: Trainable Fractional Quantization
Dongsoo Lee (Samsung Research) · Se Jung Kwon (Samsung Research) · Byeongwook Kim (Samsung Research) · Yongkweon Jeon (Samsung Research) · Baeseong Park (samsung research) · Jeongin Yun (Samsung Research)

Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms
Hilal Asi (Stanford University) · John Duchi (Stanford)

Nonconvex Sparse Graph Learning under Laplacian-structured Graphical Model
Jiaxi Ying (The Hong Kong University of Science and Technology) · José Vinícius de Miranda Cardoso (HKUST) · Daniel Palomar (The Hong Kong University of Science and Technology)

Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning
Youngsung Kim (Samsung Advanced Institute of Technology) · Jinwoo Shin (KAIST) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics) · Sung Ju Hwang (KAIST, AITRICS)

MATE: Plugging in Model Awareness to Task Embedding for Meta Learning
Xiaohan Chen (University of Texas at Austin) · Zhangyang Wang (University of Texas at Austin) · Siyu Tang (ETH Zurich) · Krikamol Muandet (Max Planck Institute for Intelligent Systems)

Cross-lingual Retrieval for Iterative Self-Supervised Training
Chau Tran (Facebook AI) · Yuqing Tang (Facebook AI) · Xian Li (Facebook) · Jiatao Gu (Facebook AI Research)

Mutual exclusivity as a challenge for deep neural networks
Kanishk Gandhi (New York University) · Brenden Lake (New York University)

Optimal Prediction of the Number of Unseen Species with Reproducibility
Yi Hao (University of California, San Diego) · Ping Li (Baidu Research USA)

Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
Alon Talmor (Allen Institute for AI, Tel Aviv University) · Oyvind Tafjord (Allen Institute for AI) · Peter Clark (Allen Institute for AI) · Yoav Goldberg (Allen Institute for AI, Bar Ilan University) · Jonathan Berant (Tel Aviv University)

Network Pruning via Greedy Optimization: Fast Rate and Efficient Algorithms
Mao Ye (The University of Texas at Austin) · Lemeng Wu (UT Austin) · Qiang Liu (UT Austin)

Neural Complexity Measures
Yoonho Lee (AITRICS) · Juho Lee (KAIST, AITRICS) · Sung Ju Hwang (KAIST, AITRICS) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics) · Seungjin Choi (POSTECH)

DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
Alexandre Carlier (ETH Zurich) · Martin Danelljan (ETH Zurich) · Alexandre Alahi (EPFL) · Radu Timofte (ETH Zurich)

Attribution Preservation in Network Compression for Reliable Network Interpretation
Geondo Park (Korea Advanced Institute of Science and Technology) · June Yong Yang (Korea Advanced Institute of Science and Technology) · Sung Ju Hwang (KAIST, AITRICS) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics)

Implicit Distributional Reinforcement Learning
Yuguang Yue (University of Texas at Austin) · Zhendong Wang (University of Texas, Austin) · Mingyuan Zhou (University of Texas at Austin)

On Regret with Multiple Best Arms
Yinglun Zhu (University of Wisconsin-Madison) · Robert Nowak (University of Wisconsion-Madison)

Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
Sreejith Balakrishnan (National University of Singapore) · Quoc Phong Nguyen (National University of Singapore) · Bryan Kian Hsiang Low (National University of Singapore) · Harold Soh (National University Singapore)

Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Dasol Hwang (Korea University) · Jinyoung Park (Korea University) · Sunyoung Kwon (Pusan National University) · KyungMin Kim (Seoul National University) · Jung-Woo Ha (Clova AI Research, NAVER Corp.) · Hyunwoo Kim (Korea University)

A Limitation of the PAC-Bayes Framework
Roi Livni (Tel Aviv University) · Shay Moran (Google AI Princeton)

Binary Matrix Completion with Hierarchical Graph Side Information
Adel Elmahdy (University of Minnesota) · Junhyung Ahn (KAIST) · Changho Suh (KAIST) · Soheil Mohajer (University of Minnesota)

Delay and Cooperation in Nonstochastic Linear Bandits
Shinji Ito (NEC Corporation) · Daisuke Hatano (RIKEN AIP) · Hanna Sumita (Tokyo Institute of Technology) · Kei Takemura (NEC Corporation) · Takuro Fukunaga (Chuo University, JST PRESTO, RIKEN AIP) · Naonori Kakimura (Keio University) · Ken-Ichi Kawarabayashi (National Institute of Informatics)

Learning Representations from Audio-Visual Spatial Alignment
Pedro Morgado (University of California, San Diego) · Yi Li (UC San Diego) · Nuno Nvasconcelos (UC San Diego)

Learning Guidance Rewards with Trajectory-space Smoothing
Tanmay Gangwani (University of Illinois, Urbana-Champaign) · Yuan Zhou (UIUC) · Jian Peng (University of Illinois at Urbana-Champaign)

Chaos, Extremism and Optimism: Volume Analysis of Learning in Games
Yun Kuen Cheung (Singapore University of Technology and Design) · Georgios Piliouras (Singapore University of Technology and Design)

EPOC: A Provably Correct Policy Gradient Approach to Reinforcement Learning
Alekh Agarwal (Microsoft Research) · Mikael Henaff (Microsoft) · Sham Kakade (University of Washington & Microsoft Research) · Wen Sun (Microsoft Research NYC)

A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
Jianfei Chen (UC Berkeley) · Yu Gai (UC Berkeley) · Zhewei Yao (UC Berkeley) · Michael W Mahoney (UC Berkeley) · Joseph Gonzalez (UC Berkeley)

Tight last-iterate convergence rates for no-regret learning in multi-player games
Noah Golowich (Massachusetts Institute of Technology) · Sarath Pattathil (Massachusetts Institute of Technology) · Constantinos Daskalakis (MIT)

Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions
Yi Hao (University of California, San Diego) · Alon Orlitsky (University of California, San Diego)

Group-Fair Online Allocation in Continuous Time
Semih Cayci (The Ohio State University) · Swati Gupta (Georgia Institute of Technology) · Atilla Eryilmaz ()

The Primal-Dual method for Learning Augmented Algorithms
Etienne Bamas (EPFL) · Andreas Maggiori (EPFL) · Ola Svensson (EPFL)

Co-exposure Maximization in Online Social Networks
Sijing Tu (KTH Royal Institute of Technology) · Cigdem Aslay (Aarhus University) · Aristides Gionis (KTH Royal Institute of Technology)

Inductive Quantum Embedding
Santosh Kumar Srivastava (IBM Research AI) · Dinesh Khandelwal (IBM Research AI) · Dhiraj Madan (IBM Research) · Dinesh Garg (IBM Research AI, India) · Hima Karanam (IBM Research AI) · L Venkata Subramaniam (IBM Research AI - India)

Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations
Zhuoran Yang (Princeton) · Chi Jin (Princeton University) · Zhaoran Wang (Northwestern University) · Mengdi Wang (Princeton University) · Michael Jordan (UC Berkeley)

Predictive Information Accelerates Learning in RL
Kuang-Huei Lee (Google) · Ian Fischer (Google) · Anthony Liu (University of Michigan) · Yijie Guo (University of Michigan) · Honglak Lee (Google Brain) · John Canny (UC Berkeley) · Sergio Guadarrama (Google Research)

Decoupled Policy Gradient Methods for Competitive Reinforcement Learning
Constantinos Daskalakis (MIT) · Dylan Foster (MIT) · Noah Golowich (Massachusetts Institute of Technology)

Partial Optimal Tranport with applications on Positive-Unlabeled Learning
Laetitia Chapel (IRISA) · Mokhtar Z. Alaya (LITIS Lab, University Rouen Normandy) · Gilles Gasso (LITIS - INSA de Rouen)

Just Pick a Sign: Reducing Gradient Conflict in Deep Networks with Gradient Sign Dropout
Zhao Chen (Waymo LLC) · Jiquan Ngiam (Google Brain) · Yanping Huang (Google Brain) · Thang Luong (Google Brain) · Henrik Kretzschmar (Waymo) · Yuning Chai (Waymo) · Dragomir Anguelov (Waymo)

Learning Long-Term Dependencies in Irregularly-Sampled Time Series
Mathias Lechner (IST Austria) · Ramin Hasani (MIT)

Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss
Shuang Qiu (University of Michigan) · Xiaohan Wei (University of Southern California) · Zhuoran Yang (Princeton) · Jieping Ye (University of Michigan) · Zhaoran Wang (Northwestern University)

Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates
Wenhao Luo (Carnegie Mellon University) · Wen Sun (Microsoft Research NYC) · Ashish Kapoor (Microsoft)

Improving Sparse Vector Technique with Renyi Differential Privacy
Yuqing Zhu (University of California Santa Barbara) · Yu-Xiang Wang (UC Santa Barbara)

Differentiable Bandit Exploration
Craig Boutilier (Google) · Chih-wei Hsu ( Google Research) · Branislav Kveton (Google Research) · Martin Mladenov (Google) · Csaba Szepesvari (DeepMind / University of Alberta) · Manzil Zaheer (Google Research)

Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces
Akash Saha (Indian Institute of Technology Bombay) · Balamurugan Palaniappan (Indian Institute of Technology Bombay)

Adversarial Counterfactual Learning and Evaluation for Recommender System
Da Xu (Walmart Labs) · Chuanwei Ruan (Walmart Labs) · Evren Korpeoglu (Walmart Labs) · Sushant Kumar (Walmart Labs) · Kannan Achan (Walmart Labs)

Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks
Jinseok Kim (Pohang University of Science and Technology (POSTECH)) · Kyungsu Kim (POSTECH) · Jae-Joon Kim (POSTECH)

SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm
Yi Hao (University of California, San Diego) · Ayush Jain (UC San Diego) · Alon Orlitsky (University of California, San Diego) · Vaishakh Ravindrakumar (UC San Diego)

Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
Karl Pertsch (University of Southern California) · Oleh Rybkin (University of Pennsylvania) · Frederik Ebert (UC Berkeley) · Shenghao Zhou (University of Pennsylvania) · Dinesh Jayaraman (University of Pennsylvania) · Chelsea Finn (Stanford) · Sergey Levine (UC Berkeley)

Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
Taiji Suzuki (The University of Tokyo/RIKEN-AIP)

Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
Anthony Tompkins (The University of Sydney) · Rafael Oliveira (The University of Sydney) · Fabio Ramos (University of Sydney, NVIDIA)

Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization
Jonathan Lacotte (Stanford University) · Mert Pilanci (Stanford)

Batch normalization provably avoids ranks collapse for randomly initialised deep networks
Hadi Daneshmand (Inria) · Jonas Kohler (ETHZ) · Francis Bach (INRIA - Ecole Normale Superieure) · Thomas Hofmann (ETH Zurich) · Aurelien Lucchi (ETH Zurich)

Hierarchical nucleation in deep neural networks
Diego Doimo (International School for Advanced Studies (SISSA)) · Aldo Glielmo (International School for Advanced Studies (SISSA))) · Alessio Ansuini (International School for Advanced Studies (SISSA)) · Alessandro Laio (International School for Advanced Studies (SISSA))

Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity
Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Sham Kakade (University of Washington & Microsoft Research) · Tamer Basar (University of Illinois at Urbana-Champaign) · Lin Yang (UCLA)

Model Selection for Production System via Automated Online Experiments
Zhenwen Dai (Spotify) · Praveen Chandar (Spotify) · Ghazal Fazelnia (Spotify Research) · Benjamin Carterette (Spotify) · Mounia Lalmas (Spotify)

Graph Geometry Interaction Learning
Shichao Zhu (Institute of Information Engineering, Chinese Academy of Sciences) · Shirui Pan (Monash University) · Chuan Zhou (Chinese Academy of Sciences) · Jia Wu (Macquarie University) · Yanan Cao (Institute of Information Engineering, Chinese Academy of Sciences) · Bin Wang (Xiaomi AI Lab)

A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
Ziqi Ke (University of Texas at Austin) · Haris Vikalo (The University of Texas at Austin)

Robust Covariate Shift without Parameter Tuning
Bijan Mazaheri (California Institute of Technology) · Siddharth Jain (Caltech) · Jehoshua Bruck (Caltech)

Modeling Noisy Annotations for Crowd Counting
Jia Wan (City University of Hong Kong) · Antoni Chan (City University of Hong Kong)

Neural Sparse Voxel Fields
Lingjie Liu (Max Planck Institute for Informatics) · Jiatao Gu (Facebook AI Research) · Kyaw Zaw Lin (National University of Singapore) · Tat-Seng Chua (National university of Singapore) · Christian Theobalt (MPI Informatik)

Federated Accelerated Stochastic Gradient Descent
Honglin Yuan (Stanford University) · Tengyu Ma (Stanford University)

On the Convergence of Smooth Regularized Approximate Value Iteration Schemes
Elena Smirnova (Criteo) · Elvis Dohmatob (Criteo)

Breaking the Communication-Privacy-Accuracy Trilemma
Wei-Ning Chen (Stanford University) · Peter Kairouz (Google) · Ayfer Ozgur (Stanford University)

Learning Manifold Implicitly via Explicit Heat-Kernel Learning
Yufan Zhou (University at Buffalo) · Changyou Chen (University at Buffalo) · Jinhui Xu (SUNY at Buffalo)

Do Adversarially Robust ImageNet Models Transfer Better?
Hadi Salman (Microsoft Research AI) · Andrew Ilyas (MIT) · Logan Engstrom (MIT) · Ashish Kapoor (Microsoft) · Aleksander Madry (MIT)

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems
Junchi Yang (University of Illinois) · Negar Kiyavash (École Polytechnique Fédérale de Lausanne) · Niao He (UIUC)

A shooting formulation of deep learning
François-Xavier Vialard (University Gustave Eiffel) · Roland Kwitt (University of Salzburg) · Susan Wei (University of Melbourne) · Marc Niethammer (UNC Chapel Hill)

PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals
Henry Charlesworth (University of Warwick) · Giovanni Montana (University of Warwick)

Improving Generalization in Reinforcement Learning with Mixture Regularization
KAIXIN WANG (National University of Singapore) · Bingyi Kang (National University of Singapore) · Jie Shao (Fudan University) · Jiashi Feng (National University of Singapore)

Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
Kristopher Jensen (University of Cambridge) · Ta-Chu Kao (University of Cambridge) · Marco Tripodi (MRC) · Guillaume Hennequin (Cambridge)

Scalable Belief Propagation via Relaxed Scheduling
Vitalii Aksenov (IST Austria) · Dan Alistarh (IST Austria & Neural Magic Inc.) · Janne Korhonen (IST Austria)

Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming
Vo Nguyen Le Duy (Nagoya Institute of Technology / RIKEN) · Hiroki Toda (Nagoya Institute of Technology) · Ryota Sugiyama (Nagoya Institute of Technology) · Ichiro Takeuchi (Nagoya Institute of Technology)

Gradient Estimation with Stochastic Softmax Tricks
Max Paulus (ETH Zurich) · Dami Choi (University of Toronto) · Daniel Tarlow (Google Brain) · Andreas Krause (ETH Zurich) · Chris J. Maddison (University of Toronto)

Watch out! Motion is Blurring the Vision of Your Deep Neural Networks
Qing Guo (Nanyang Technological University) · Felix Juefei-Xu (Alibaba Group) · Xiaofei Xie (Nanyang Technological University) · Lei Ma (Kyushu University, Japan) · Jian Wang (Nanyang Technological University) · Bing Yu (Kyushu university) · Wei Feng (Tianjin University) · Yang Liu (Nanyang Technology University, Singapore)

Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Vu Nguyen (University of Oxford) · Vaden Masrani (University of British Columbia) · Rob Brekelmans (University of Southern California) · Michael A Osborne (U Oxford) · Frank Wood (University of British Columbia)

Fairness without Demographics through Adversarially Reweighted Learning
Preethi Lahoti (Max Planck Institute for Informatics, Germany) · Alex Beutel (Google) · Jilin Chen (Google Brain) · Kang Lee (Google Research) · Flavien Prost (Google) · Nithum Thain (Google) · Xuezhi Wang (Google) · Ed Chi (Google Inc.)

Neural Power Units
Niklas Maximilian Heim (Czech Technical University) · Tomas Pevny (Czech Technical University) · Vasek Smidl (Czech Technical University in Prague)

RD$^2$: Reward Decomposition with Representation Decomposition
Zichuan Lin (Tsinghua University) · Derek Yang (UC San Diego) · Li Zhao (Microsoft Research) · Tao Qin (Microsoft Research) · Guangwen Yang (Tsinghua University) · Tie-Yan Liu (Microsoft Research Asia)

Joint Contrastive Learning with Infinite Possibilities
Qi Cai (University of Science and Technology of China) · Yu Wang (JD AI Research) · Yingwei Pan (JD AI Research) · Ting Yao (JD AI Research) · Tao Mei (AI Research of JD.com)

Learning Fair and Transferable Representations
Luca Oneto (University of Genoa) · Michele Donini (Amazon) · Giulia Luise (University College London) · Carlo Ciliberto (Imperial College London) · Massimiliano Pontil (IIT) · Andreas Maurer ()

Understanding the Role of Training Regimes in Continual Learning
Seyed Iman Mirzadeh (Washington State University) · Mehrdad Farajtabar (DeepMind) · Razvan Pascanu (Google DeepMind) · Hassan Ghasemzadeh (Washington State University)

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
Panayotis Mertikopoulos (CNRS (French National Center for Scientific Research)) · Nadav Hallak (EPFL) · Ali Kavis (EPFL) · Volkan Cevher (EPFL)

Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform
Jonathan Lacotte (Stanford University) · Sifan Liu (Stanford University) · Edgar Dobriban (University of Pennsylvania) · Mert Pilanci (Stanford)

Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
Guilin Li (Huawei Noah's Ark Lab) · Junlei Zhang (Huawei Noah’s Ark Lab) · Yunhe Wang (Huawei Noah's Ark Lab) · Chuanjian Liu (Huawei Noah's Ark Lab) · Matthias Tan (CityU) · Yunfeng Lin (Shanghai Jiao Tong University) · Wei Zhang (Noah's Ark Lab, Huawei Inc.) · Jiashi Feng (National University of Singapore) · Tong Zhang (Tencent AI Lab)

Contrastive Generative Adversarial Networks
Minguk Kang (POSTECH) · Jaesik Park (POSTECH)

(De)Randomized Smoothing for Certifiable Defense against Patch Attacks
Alexander Levine (University of Maryland, College Park) · Soheil Feizi (University of Maryland)

What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes
Herman Ho-Man Yau (University of Surrey) · Chris Russell (The Alan Turing Institute/ The University of Surrey) · Simon Hadfield (University of Surrey)

Field-wise Learning for Multi-field Categorical Data
Zhibin Li (University of Technology Sydney ) · Jian Zhang (UTS) · Yongshun Gong (University of Technology Sydney) · Yazhou Yao (Nanjing University of Science and Technology) · Qiang Wu (University of Technology Sydney)

What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Vitaly Feldman (Google Brain) · Chiyuan Zhang (Google Brain)

A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
Arnu Pretorius (InstaDeep) · Scott Cameron (Instadeep) · Elan van Biljon (Stellenbosch University) · Thomas Makkink (InstaDeep) · Shahil Mawjee (InstaDeep) · Jeremy du Plessis (University of Cape Town) · Jonathan Shock (University of Cape Town) · Alexandre Laterre (InstaDeep) · Karim Beguir (InstaDeep)

Hierarchical Poset Decoding for Compositional Generalization in Language
Yinuo Guo (Peking University) · Zeqi Lin (Microsoft) · Jian-Guang Lou (Microsoft) · Dongmei Zhang (Microsoft Research)

Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate
Zhiyuan Li (Princeton University) · Kaifeng Lyu (Tsinghua University) · Sanjeev Arora (Princeton University)

Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint
Georgios Amanatidis (University of Essex) · Federico Fusco (Sapienza University of Rome) · Philip Lazos (Sapienza University of Rome) · Stefano Leonardi (Sapienza University of Rome) · Rebecca Reiffenhäuser (Sapienza University of Rome)

Kernel Alignment Risk Estimator: Risk Prediction from Training Data
Arthur Jacot (EPFL) · Berfin Simsek (EPFL) · Francesco Spadaro (EPFL) · Clement Hongler (EPFL) · Franck Gabriel (EPFL)

Planning in Markov Decision Processes with Gap-Dependent Sample Complexity
Anders Jonsson (Universitat Pompeu Fabra) · Emilie Kaufmann (CNRS) · Pierre Menard (Inria) · Omar Darwiche Domingues (Inria) · Edouard Leurent (INRIA) · Michal Valko (DeepMind)

Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
David Novotny (Facebook AI Research) · Roman Shapovalov (Facebook AI Research) · Andrea Vedaldi (University of Oxford / Facebook AI Research)

Bandit Samplers for Training Graph Neural Networks
Ziqi Liu (Ant Group) · Zhengwei Wu (Ant Financial) · Zhiqiang Zhang (Ant Financial Services Group) · Jun Zhou (Ant Financial) · Shuang Yang (Ant Financial) · Le Song (Ant Financial Services Group) · Yuan Qi (Ant Financial Services Group)

Latent World Models For Intrinsically Motivated Exploration
Aleksandr Ermolov (University of Trento) · Nicu Sebe (University of Trento)

Cycle-Contrast for Self-Supervised Video Representation Learning
Quan Kong (Hitachi,Ltd.) · Wenpeng Wei (Hitachi, Ltd.) · Ziwei Deng (Hitachi,Ltd.) · Tomoaki Yoshinaga (Hitachi, Ltd.) · Tomokazu Murakami (Hitachi,Ltd.)

Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
Yunqiu Xu (University of Technology Sydney) · Meng Fang (Tencent) · Ling Chen (" University of Technology, Sydney, Australia") · Yali Du (University College London) · Joey Tianyi Zhou (IHPC, A*STAR) · Chengqi Zhang (University of Technology Sydney)

Instance Based Approximations to Profile Maximum Likelihood
Nima Anari (Stanford) · Moses Charikar (Stanford University) · Kirankumar Shiragur (Stanford University) · Aaron Sidford (Stanford)

CoMIR: Contrastive Multimodal Image Representation for Registration
Nicolas Pielawski (Uppsala University) · Elisabeth Wetzer (Centre for Image Analysis, Department of Information Technology, Uppsala University, Sweden) · Johan Öfverstedt (Department of Information Technology, Uppsala University) · Jiahao Lu (Uppsala University) · Carolina Wählby (Uppsala University) · Joakim Lindblad (Centre for Image Analysis, Department of Information Technology, Uppsala University, Sweden) · Natasa Sladoje (Centre for Image Analysis, Department of Information Technology, Uppsala University, Sweden)

Counterfactual Vision-and-Language Navigation: Unravelling the Unseen
Amin Parvaneh (University of Adelaide) · Ehsan Abbasnejad (University of Adelaide) · Damien Teney (University of Adelaide) · Qinfeng Shi (University of Adelaide) · Anton van den Hengel (University of Adelaide)

Language Through a Prism: A Spectral Approach for Multiscale Language Representations
Alex Tamkin (Stanford University) · Dan Jurafsky (Stanford University) · Noah Goodman (Stanford University)

Dark Experience for General Continual Learning: a Strong, Simple Baseline
Pietro Buzzega (University of Modena and Reggio Emilia) · Matteo Boschini (University of Modena and Reggio Emilia) · Angelo Porrello (University of Modena and Reggio Emilia) · Davide Abati (University of Modena and Reggio Emilia) · SIMONE CALDERARA (University of Modena and Reggio Emilia, Italy)

Bidirectional Convolutional Poisson Gamma Dynamical Systems
wenchao chen (Xidian university) · Chaojie Wang (Xidian University) · Bo Chen (Xidian University) · Yicheng Liu (Xidian university) · Hao Zhang (Xidian University) · Mingyuan Zhou (University of Texas at Austin)

3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
Benjamin Biggs (University of Cambridge) · David Novotny (Facebook AI Research) · Sebastien Ehrhardt (University of Oxford) · Hanbyul Joo (FAIR) · Ben Graham (Facebook Research) · Andrea Vedaldi (University of Oxford / Facebook AI Research)

Language Models are Few-Shot Learners
Tom B Brown (Google Brain) · Benjamin Mann (OpenAI) · Nick Ryder (OpenAI) · Melanie Subbiah (OpenAI) · Jared D Kaplan (Johns Hopkins University) · Prafulla Dhariwal (OpenAI) · Arvind Neelakantan (OpenAI) · Pranav Shyam (OpenAI) · Girish Sastry (OpenAI) · Amanda Askell (OpenAI) · Sandhini Agarwal (OpenAI) · Ariel Herbert-Voss (OpenAI) · Gretchen M Krueger (OpenAI) · Tom Henighan (OpenAI) · Rewon Child (OpenAI) · Aditya Ramesh (OpenAI) · Daniel Ziegler (OpenAI) · Jeffrey Wu (OpenAI) · Clemens Winter (OpenAI) · Chris Hesse (OpenAI) · Mark Chen (OpenAI) · Eric Sigler (OpenAI) · Mateusz Litwin (OpenAI) · Scott Gray (OpenAI) · Benjamin Chess (OpenAI) · Jack Clark (OpenAI) · Christopher Berner (OpenAI) · Sam McCandlish (OpenAI) · Alec Radford (OpenAI) · Ilya Sutskever (OpenAI) · Dario Amodei (OpenAI)

Online Planning with Lookahead Policies
Yonathan Efroni (Technion) · Mohammad Ghavamzadeh (Google Research) · Shie Mannor (Technion)

Variational Bayesian Unlearning
Quoc Phong Nguyen (National University of Singapore) · Bryan Kian Hsiang Low (National University of Singapore) · Patrick Jaillet (MIT)

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
Parameswaran Kamalaruban (EPFL) · Yu-Ting Huang (EPFL) · Ya-Ping Hsieh (EPFL) · Paul Rolland (EPFL) · Cheng Shi (Unversity of Basel) · Volkan Cevher (EPFL)

On the Power of Louvain for Graph Clustering
Vincent Cohen-Addad (CNRS & Sorbonne Université) · Adrian Kosowski (NavAlgo) · Frederik Mallmann-Trenn (King's College London) · David Saulpic (Ecole normale supérieure)

Interferobot: aligning an optical interferometer by a reinforcement learning agent
Dmitry Sorokin (Russian Quantum Center) · Alexander Ulanov (Russian Quantum Center) · Ekaterina Sazhina (Russian Quantum Center) · Alexander Lvovsky (Oxford University)

Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Andrew Foong (University of Cambridge) · Wessel Bruinsma (University of Cambridge and Invenia Labs) · Jonathan Gordon (University of Cambridge) · Yann Dubois (Facebook AI Research) · James Requeima (University of Cambridge / Invenia Labs) · Richard E Turner (University of Cambridge)

Object-Centric Learning with Slot Attention
Francesco Locatello (ETH Zürich - MPI Tübingen) · Dirk Weissenborn (Google) · Thomas Unterthiner (Google Research, Brain Team) · Aravindh Mahendran (Google) · Georg Heigold (Google) · Jakob Uszkoreit (Google, Inc.) · Alexey Dosovitskiy (Google Research) · Thomas Kipf (Google Research)

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
Bertrand Charpentier (Technical University of Munich) · Daniel Zügner (Technical University of Munich) · Stephan Günnemann (Technical University of Munich)

Margins are Insufficient for Explaining Gradient Boosting
Allan Grønlund (Aarhus University, MADALGO) · Lior Kamma (Aarhus University) · Kasper Green Larsen (Aarhus University)

Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning
Jean-Bastien Grill (DeepMind) · Florian Strub (DeepMind) · Florent Altché (DeepMind) · Corentin Tallec (Deepmind) · Pierre Richemond (Imperial College) · Elena Buchatskaya (DeepMind) · Carl Doersch (DeepMind) · Bernardo Avila Pires (DeepMind) · Zhaohan Guo (DeepMind) · Mohammad Gheshlaghi Azar (DeepMind) · Bilal Piot (DeepMind) · koray kavukcuoglu (DeepMind) · Remi Munos (DeepMind) · Michal Valko (DeepMind)

Multimodal Graph Networks for Compositional Generalization in Visual Question Answering
Raeid Saqur (Princeton University) · Karthik Narasimhan (Princeton University)

Reinforcement Learning for Control with Multiple Frequencies
Jongmin Lee (KAIST) · ByungJun Lee (KAIST) · Kee-Eung Kim (KAIST)

CrossTransformers: spatially-aware few-shot transfer
Carl Doersch (DeepMind) · Ankush Gupta (DeepMind) · Andrew Zisserman (DeepMind & University of Oxford)

Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning
Chandrashekar Lakshminarayanan (Indian Institute of Technology, Palakkad) · Amit Vikram Singh (Indian Institute Of Technology, Palakkad)

On the Expressiveness of Approximate Inference in Bayesian Neural Networks
Andrew Foong (University of Cambridge) · David Burt (University of Cambridge) · Yingzhen Li (Microsoft Research Cambridge) · Richard E Turner (University of Cambridge)

A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks
Zixiang Chen (UCLA) · Yuan Cao (UCLA) · Quanquan Gu (UCLA) · Tong Zhang (Tencent AI Lab)

Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning
Jaehyung Kim (KAIST) · Youngbum Hur (Samsung Advanced Institute of Technology) · Sejun Park (KAIST) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics) · Sung Ju Hwang (KAIST, AITRICS) · Jinwoo Shin (KAIST)

SMYRF - Efficient attention using asymmetric clustering
Giannis Daras (National Technical University of Athens) · Nikita Kitaev (University of California, Berkeley) · Augustus Odena (Google Brain) · Alexandros Dimakis (University of Texas, Austin)

Understanding and Improving Fast Adversarial Training
Maksym Andriushchenko (EPFL) · Nicolas Flammarion (EPFL)

Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization
Yan Yan (the University of Iowa) · Yi Xu (Alibaba Group U.S. Inc.) · Qihang Lin (University of Iowa) · Wei Liu (Tencent AI Lab) · Tianbao Yang (The University of Iowa)

Learning Restricted Boltzmann Machines with Few Latent Variables
Guy Bresler (MIT) · Rares-Darius Buhai (ETH Zurich)

Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
Nikolaos Karalias (EPFL) · Andreas Loukas (EPFL)

DynaBERT: Dynamic BERT with Adaptive Width and Depth
Lu Hou (Huawei Noah's Ark Lab) · Zhiqi Huang (Peking University) · Lifeng Shang (Huawei Noah's Ark Lab) · Xin Jiang (Huawei Noah's Ark Lab) · Xiao Chen (Huawei Noah's Ark Lab) · Qun Liu (Huawei Noah's Ark Lab)

Inverting Gradients - How easy is it to break privacy in federated learning?
Jonas Geiping (University of Siegen) · Hartmut Bauermeister (University of Siegen) · Hannah Dröge (University of Siegen) · Michael Moeller (University of Siegen)

Guide Learning by Dynamic Instance Hardness
Tianyi Zhou (University of Washington, Seattle) · Shengjie Wang ("University of Washington, Seattle") · Jeff Bilmes (University of Washington, Seattle)

MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics
Luca De Gennaro Aquino (Grenoble Ecole de Management) · Stephan Eckstein (University of Konstanz)

MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles
Zhennan Wang (Shenzhen University) · Canqun Xiang (Shenzhen University) · Wenbin Zou (Shenzhen University) · Chen Xu (Shenzhen University)

Recursive Inference for Variational Autoencoders
Minyoung Kim (Samsung AI Center Cambridge) · Vladimir Pavlovic (Rutgers University)

How do fair decisions fare in long-term qualification?
Xueru Zhang (University of Michigan) · Ruibo Tu (KTH Royal Institute of Technology) · Yang Liu (UC Santa Cruz) · mingyan liu (university of Michigan, Ann Arbor) · Hedvig Kjellstrom (KTH Royal Institute of Technology) · Kun Zhang (CMU) · Cheng Zhang (Microsoft Research, Cambridge, UK)

Collegial Ensembles
Etai Littwin (Apple) · Ben Myara (apple) · Sima Sabah (Apple) · Joshua M Susskind (Apple Inc.) · Shuangfei Zhai (Apple) · Oren Golan (apple)

Learning to Play Sequential Games versus Unknown Opponents
Pier Giuseppe Sessa (ETH Zürich) · Ilija Bogunovic (ETH Zurich) · Maryam Kamgarpour (ETH Zürich) · Andreas Krause (ETH Zurich)

Continual Learning in Low-rank Orthogonal Subspaces
Arslan Chaudhry (University of Oxford) · Naeemullah Khan (University of Oxford) · Puneet Dokania (University of Oxford) · Philip Torr (University of Oxford)

Stochastic Normalizing Flows
Hao Wu (Freie Universität Berlin) · Jonas Köhler (Freie Universität Berlin) · Frank Noe (FU Berlin)

Contextual Games: Multi-Agent Learning with Side Information
Pier Giuseppe Sessa (ETH Zürich) · Ilija Bogunovic (ETH Zurich) · Andreas Krause (ETH Zurich) · Maryam Kamgarpour (ETH Zürich)

Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
Amir Rahimi (Australian National University) · Amirreza Shaban (Georgia Institute of Technology) · Ching-An Cheng (Microsoft) · Richard I Hartley (Australian National University) · Byron Boots (University of Washington)

Bayesian Robust Optimization for Imitation Learning
Daniel Brown (The University of Texas at Austin) · Scott Niekum (UT Austin) · Marek Petrik (University of New Hampshire)

Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method
Qi Zhou (University of Science and Technology of China) · Yufei Kuang (University of Science and Technology of China) · Zherui Qiu (University of Science and Technology of China) · Houqiang Li (University of Science and Technology of China) · Jie Wang (University of Science and Technology of China)

Benchmarking Deep Learning Interpretability in Time Series Predictions
Aya Abdelsalam Ismail (University of Maryland) · Mohamed Gunady (University of Maryland) · Hector Corrada Bravo (University of Maryland) · Soheil Feizi (University of Maryland)

Nimble: Lightweight Execution of Deep Neural Networks on a GPU
Woosuk Kwon (Seoul National University) · Gyeong-In Yu (Seoul National University) · Eunji Jeong (Seoul National Univerity) · Byung-Gon Chun (Seoul National University)

Teaching a GAN What Not to Learn
Siddarth Asokan (Indian Institute of Science) · Chandra Seelamantula (IISc Bangalore)

Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network
Lifeng Shen (The Hong Kong University of Science and Technology) · Zhuocong Li (Tencent) · James Kwok (Hong Kong University of Science and Technology)

Fast and Accurate $k$-means++ via Rejection Sampling
Vincent Cohen-Addad (CNRS & Sorbonne Université) · Silvio Lattanzi (Google Research) · Ashkan Norouzi-Fard (Google Research) · Christian Sohler (University of Cologne) · Ola Svensson (EPFL)

Factor Graph Neural Networks
Zhen Zhang (University of Adelaide) · Fan Wu (Nanjing University) · Wee Sun Lee (National University of Singapore)

Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret
Yingjie Fei (Cornell University) · Zhuoran Yang (Princeton) · Yudong Chen (Cornell University) · Zhaoran Wang (Northwestern University) · Qiaomin Xie (Cornell University)

Regularizing Towards Permutation Invariance In Recurrent Models
Edo Cohen-Karlik (Tel Aviv University) · Avichai Ben David (Tel Aviv University) · Amir Globerson (Tel Aviv University, Google)

Deep Archimedean Copulas
Chun Kai Ling (Carnegie Mellon University) · Fei Fang (Carnegie Mellon University) · J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

On Convergence of Nearest Neighbor Classifiers over Feature Transformations
Luka Rimanic (ETH Zurich) · Cedric Renggli (ETH Zurich) · Bo Li (UIUC) · Ce Zhang (ETH Zurich)

Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering
Long Chen (Nanjing University) · Yuan Yao (Nanjing University) · Hanghang Tong (University of Illinois at Urbana-Champaign) · Miao Xu (RIKEN AIP) · Feng Xu (Nanjing University)

Non-parametric Models for Non-negative Functions
Ulysse Marteau-Ferey (DI ENS / INRIA) · Francis Bach (INRIA - Ecole Normale Superieure) · Alessandro Rudi (INRIA, Ecole Normale Superieure)

Online Sinkhorn: Optimal Transport distances from sample streams
Arthur Mensch (ENS) · Gabriel Peyré (CNRS and ENS)

Online Learning in Contextual Bandits using Gated Linear Networks
Eren Sezener (DeepMind) · Marcus Hutter (DeepMind) · David Budden (DeepMind) · Jianan Wang (DeepMind) · Joel Veness (Deepmind)

Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs
Lin Liu (University of Science and Technology of China) · Shanxin Yuan (Huawei Technologies Research and Development (UK)) · Jianzhuang Liu (Huawei Noah's Ark Lab) · Liping Bao (University of Science and Technology of China) · Gregory Slabaugh (Huawei Noah's Ark Lab) · Qi Tian (Huawei Noah’s Ark Lab)

Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring
Taira Tsuchiya (The University of Tokyo) · Junya Honda (The Univerisity of Tokyo / RIKEN) · Masashi Sugiyama (RIKEN / University of Tokyo)

Robust Density Estimation under Besov IPM Losses
Ananya Uppal (Carnegie Mellon University) · Shashank Singh (Google) · Barnabas Poczos (Carnegie Mellon University)

Stochastic Optimization with Laggard Data Pipelines
Naman Agarwal (Google) · Rohan Anil (Google) · Tomer Koren (Tel Aviv University & Google) · Kunal Talwar (Apple) · Cyril Zhang (Princeton University)

Learning to Detect Objects with a 1 Megapixel Event Camera
Etienne Perot (PROPHESEE) · Pierre de Tournemire (PROPHESEE) · Davide Nitti (PROPHESEE) · Jonathan Masci (NNAISENSE) · Amos Sironi (PROPHESEE)

Telescoping Density-Ratio Estimation
Benjamin Rhodes (University of Edinburgh) · Kai Xu (University of Edinburgh) · Michael U. Gutmann (University of Edinburgh)

GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
Dingfan Chen (CISPA - Helmholtz Center for Information Security) · Tribhuvanesh Orekondy (Max Planck Institute for Informatics) · Mario Fritz (CISPA Helmholtz Center i.G.)

An efficient nonconvex reformulation of stagewise convex optimization problems
Srinadh Bhojanapalli (Google Research) · Rudy Bunel (Deepmind) · Krishnamurthy Dvijotham (DeepMind) · Oliver Hinder (University of Pittsburgh)

Probabilistic orientation estimation with matrix Fisher distributions
David A Mohlin (KTH) · Josephine Sullivan (KTH Royal Institute of Technology) · Gérald Bianchi (Tobii AB)

Learning from Failure: De-biasing Classifier from Biased Classifier
Junhyun Nam (KAIST) · Hyuntak Cha (KAIST) · Sung-Soo Ahn (KAIST) · Jaeho Lee (KAIST) · Jinwoo Shin (KAIST)

Coresets for Regressions with Panel Data
Lingxiao Huang (Huawei) · K Sudhir (Yale University) · Nisheeth Vishnoi (Yale University)

Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration
Cédric Colas (INRIA) · Tristan Karch (Inria) · Nicolas Lair (Inserm Robot Cognition Lab) · Jean-Michel Dussoux (Cloud Temple) · Clément Moulin-Frier (Inria) · Peter F Dominey (INSERM/CNRS) · Pierre-Yves Oudeyer (INRIA)

High-recall causal discovery for autocorrelated time series with latent confounders
Andreas Gerhardus (German Aerospace Center (DLR)) · Jakob Runge (Institute of Data Science, German Aerospace Center (DLR))

Focus of Attention Improves Information Transfer in Visual Features
Matteo Tiezzi (University of Siena) · Stefano Melacci (University of Siena) · Alessandro Betti (University of Siena) · Marco Maggini (University of Siena) · Marco Gori (University of Siena)

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting
Richard Kurle (Volkswagen Group) · Syama Sundar Rangapuram (Amazon Research) · Emmanuel de Bézenac (Sorbonne Université) · Stephan Günnemann (Technical University of Munich) · Jan Gasthaus (Amazon.com)

Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
Aaron Sonabend (Harvard University) · Junwei Lu () · Leo Anthony Celi (Massachusetts Institute of Technology) · Tianxi Cai (Harvard School of Public Health) · Peter Szolovits (MIT)

Correspondence learning via linearly-invariant embedding
Riccardo Marin (University of Verona) · Marie-Julie Rakotosaona (Ecole Polytechnique) · Simone Melzi (University of Verona) · Maks Ovsjanikov (Ecole polytechnique)

Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
Joao Marques-Silva (ANITI, Federal University of Toulouse Midi-Pyrénées) · Thomas Gerspacher (ANITI) · Martin Cooper (University of Toulouse 3) · Alexey Ignatiev (Monash University) · Nina Narodytska (VMmare Research)

Robustness of Community Detection to Random Geometric Perturbations
Sandrine Peche (LPSM, Université Paris Diderot) · Vianney Perchet (ENSAE & Criteo AI Lab)

Optimal visual search based on a model of target detectability in natural images
Shima Rashidi (The University of Melbourne) · Krista A Ehinger (The University of Melbourne) · Andrew Turpin (University of Melbourne) · Lars Kulik (University of Melbourne)

Towards Convergence Rate Analysis of Random Forests for Classification
Wei Gao (Nanjing University) · Zhi-Hua Zhou (Nanjing University)

Dynamic allocation of limited memory resources in reinforcement learning
Nisheet Patel (University of Geneva) · Luigi Acerbi (University of Helsinki) · Alexandre Pouget (University of Geneva)

AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control
Afshin Oroojlooy (SAS Institute, Inc) · Mohammadreza Nazari (SAS Institute Inc.) · Davood Hajinezhad (SAS Institute Inc.) · Jorge Silva (SAS)

Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds
Valentin Liévin (Technical University of Denmark) · Andrea Dittadi (Technical University of Denmark) · Anders Christensen (Technical University of Denmark) · Ole Winther (DTU and KU)

Hierarchically-Organized Latent Modules for Exploratory Search in Morphogenetic Systems
Mayalen Etcheverry (INRIA) · Clément Moulin-Frier (Inria) · Pierre-Yves Oudeyer (INRIA)

H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
Thomas Limbacher (Graz University of Technology) · Robert Legenstein (Graz University of Technology)

Graph Policy Network for Transferable Active Learning on Graphs
Shengding Hu (Tsinghua University) · Zheng Xiong (Tsinghua University / University of Oxford) · Meng Qu (Mila) · Xingdi Yuan (Microsoft Research) · Marc-Alexandre Côté (Microsoft Research) · Zhiyuan Liu (Tsinghua University) · Jian Tang (Mila)

All your loss are belong to Bayes
Christian Walder (DATA61) · Richard Nock (Data61, the Australian National University and the University of Sydney)

Model Fusion via Optimal Transport
Sidak Pal Singh (EPFL) · Martin Jaggi (EPFL)

Train by Reconnect: Decoupling Locations of Weights from their Values
Yushi Qiu (The University of Tokyo) · Reiji Suda (University of Tokyo)

Identifying the signal and noise structure underlying neural population activity with Gaussian process factor models
Stephen Keeley (Princeton University) · Mikio Aoi (Princeton University) · Yiyi Yu (UNC) · Spencer Smith (UC Santa Barbara) · Jonathan W Pillow (Princeton University)

DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
Moshe Eliasof (Ben-Gurion University of the Negev) · Eran Treister (Ben-Gurion University of the Negev)

A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization
Digvijay Boob (Southern Methodist University) · Qi Deng (Shanghai University of Finance and Economics) · Guanghui Lan (Georgia Tech) · Yilin Wang (Shanghai University of Finance and Economics)

Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes
Juan Luis Gonzalez (KAIST-VICLab) · Munchurl Kim (KAIST-VICLab)

Deep Statistical Solvers
Balthazar Donon (RTE R&D / Université Paris-Saclay) · Zhengying Liu (Inria/U. Paris-Sud) · Wenzhuo LIU (Inria Paris Saclay) · Isabelle Guyon (U. Paris-Saclay & ChaLearn) · Antoine Marot (RTE) · Marc Schoenauer (INRIA / U. Paris-Saclay)

HRN: A New Approach to One Class Learning
Wenpeng Hu (Peking University) · Mengyu Wang (Peking University) · Qi Qin (Peking University) · Jinwen Ma (Peking University) · Bing Liu (Peking University)

Weak Form Generalized Hamiltonian Learning
Kevin L Course (University of Toronto) · Trefor Evans (University of Toronto) · Prasanth Nair (University of Toronto)

A Bandit Learning Algorithm and Applications to Auction Design
Kim Thang Nguyen (IBISC, University Paris-Saclay)

Hardness of Learning Neural Networks with Natural Weights
Amit Daniely (Hebrew University and Google Research) · Gal Vardi (Weizmann Institute of Science)

Deep Metric Learning with Spherical Embedding
Dingyi Zhang (Zhejiang University) · Yingming Li (Zhejiang University) · Zhongfei Zhang (Binghamton University)

Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms.
Sriram Sankaranarayanan (University of Colorado, Boulder) · Yi Chou (University of Colorado Boulder) · Eric Goubault (Ecole Polytechnique) · Sylvie Putot (Ecole Polytechnique)

Neuronal Gaussian Process Regression
Johannes Friedrich (Flatiron Institute)

Learning to solve TV regularised problems with unrolled algorithms
Hamza Cherkaoui (CEA) · Jeremias Sulam (Johns Hopkins University) · Thomas Moreau (Inria)

A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons
Gabriel Mahuas (ENS Paris-Saclay; IST Austria; LPENS) · Giulio Isacchini (Max Planck Institute for Dynamics and Selforganisation) · Olivier Marre (Institut de la vision) · Ulisse Ferrari (Universite Pier et Marie Curie) · Thierry Mora (ENS)

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
Defu Cao (Peking University) · Yujing Wang (MSRA) · Juanyong Duan (Microsoft) · Ce Zhang (ETH Zurich) · Xia Zhu (Microsoft) · Congrui Huang (Microsoft) · Yunhai Tong (Peking University) · Bixiong Xu (Microsoft) · Jing Bai (Microsoft) · Jie Tong (Microsoft) · Qi Zhang (Microsoft)

HYDRA: Pruning Adversarially Robust Neural Networks
Vikash Sehwag (Princeton University) · Shiqi Wang (Columbia) · Prateek Mittal (Princeton University) · Suman Jana (Columbia University)

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Chi Jin (Princeton University) · Sham Kakade (University of Washington & Microsoft Research) · Akshay Krishnamurthy (Microsoft) · Qinghua Liu (Princeton University)

Regression with reject option and application to kNN
Christophe Denis (Universite Paris Est) · Mohamed Hebiri (Université Gustave Eiffel) · Ahmed Zaoui (Université Gustave Eiffel)

Curvature Regularization to Prevent Distortion in Graph Embedding
Hongbin Pei (Jilin University) · Bingzhe Wei (University of Illinois at Urbana-Champaign) · Kevin Chang (University of Illinois at Urbana-Champaign) · Chunxu Zhang (Jilin University) · Bo Yang (Jilin University)

What Do Neural Networks Learn When Trained With Random Labels?
Hartmut Maennel (Google) · Ibrahim Alabdulmohsin (Google Research) · Ilya Tolstikhin (Google, Brain Team, Zurich) · Robert Baldock (Google) · Olivier Bousquet (Google Brain (Zurich)) · Sylvain Gelly (Google Brain (Zurich)) · Daniel Keysers (Google Research, Brain Team)

On Infinite-Width Hypernetworks
Etai Littwin (Apple) · Tomer Galanti (Tel Aviv University) · Lior Wolf (Facebook AI Research) · Greg Yang (Microsoft Research)

Discovering Symbolic Models from Deep Learning with Inductive Biases
Miles Cranmer (Princeton University) · Alvaro Sanchez Gonzalez (DeepMind) · Peter Battaglia (DeepMind) · Rui Xu (Princeton University) · Kyle Cranmer (New York University) · David Spergel (Flatiron Institute) · Shirley Ho (Flatiron institute)

Robust Disentanglement of a Few Factors at a Time
Benjamin Estermann (ETH Zurich) · Markus Marks (ETH Zurich) · Mehmet Fatih Yanik (ETH Zürich)

Convex optimization based on global lower second-order models
Nikita Doikov (Catholic University of Louvain) · Yurii Nesterov (Catholic University of Louvain (UCL))

Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
Sarah Perrin (Univ. Lille) · Julien Perolat (DeepMind) · Mathieu Lauriere (Princeton University) · Matthieu Geist (Google Brain) · Romuald Elie (Deepmind) · Olivier Pietquin (Google Research Brain Team)

A novel variational form of the Schatten-$p$ quasi-norm
Paris Giampouras (The Johns Hopkins University) · Rene Vidal (Johns Hopkins University, USA) · Athanasios Rontogiannis (National Observatory of Athens) · Benjamin Haeffele (Johns Hopkins University)

Improved Analysis of Clipping Algorithms for Non-convex Optimization
Bohang Zhang (Peking University) · Jikai Jin (Peking University) · Cong Fang (Peking University) · Liwei Wang (Peking University)

A polynomial-time algorithm for learning nonparametric causal graphs
Ming Gao (the University of Chicago) · Yi Ding (University of Chicago) · Bryon Aragam (University of Chicago)

Fair regression with Wasserstein barycenters
Evgenii Chzhen (Université Paris-Saclay) · Christophe Denis (Universite Paris Est) · Mohamed Hebiri (Université Gustave Eiffel) · Luca Oneto (University of Genoa) · Massimiliano Pontil (IIT)

Learning discrete distributions with infinite support
Doron Cohen (Ben-Gurion University of the Negev) · Aryeh Kontorovich (Ben Gurion University) · Geoffrey Wolfer (Ben-Gurion University of the Negev)

Curriculum learning for multilevel budgeted combinatorial problems
Adel Nabli (Université de Montréal) · Margarida Carvalho (Université de Montréal)

Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs
Feng Xie (Peking University) · Ruichu Cai (Guangdong University of Technology) · Biwei Huang (Carnegie Mellon University) · Clark Glymour (Carnegie Mellon University) · Zhifeng Hao (Guangdong University of Technology) · Kun Zhang (CMU)

Incorporating Pragmatic Reasoning Communication into Emergent Language
Yipeng Kang (Tsinghua University) · Tonghan Wang (Tsinghua University) · Gerard de Melo (Hasso Plattner Institute)

Geometric Dataset Distances via Optimal Transport
David Alvarez Melis (MIT) · Nicolo Fusi (Microsoft Research)

Sampling from a k-DPP without looking at all items
Daniele Calandriello (LCSL IIT/MIT) · Michal Derezinski (UC Berkeley) · Michal Valko (DeepMind)

AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Silviu-Marian Udrescu (MIT) · Andrew Tan (Massachusetts Institute of Technology) · Jiahai Feng (MIT) · Orisvaldo Neto (MIT) · Tailin Wu (MIT) · Max Tegmark (MIT)

Fair regression via plug-in estimator and recalibration with statistical guarantees
Evgenii Chzhen (Université Paris-Saclay) · Christophe Denis (Universite Paris Est) · Mohamed Hebiri (Université Gustave Eiffel) · Luca Oneto (University of Genoa) · Massimiliano Pontil (IIT)

PAC-Bayes Learning Bounds for Sample-Dependent Priors
Pranjal Awasthi (Rutgers University/Google) · Satyen Kale (Google) · Stefani Karp (Google/CMU) · Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research)

Sharp uniform convergence bounds through empirical centralization
Cyrus Cousins (Brown University) · Matteo Riondato (Amherst College)

Network-to-Network Translation with cINNs
Robin Rombach (Heidelberg University) · Patrick Esser (Heidelberg University) · Bjorn Ommer (Heidelberg University)

Ensemble Distillation for Robust Model Fusion in Federated Learning
Tao Lin (EPFL) · Lingjing Kong (EPFL) · Sebastian U Stich (EPFL) · Martin Jaggi (EPFL)

Spin-Weighted Spherical CNNs
Carlos Esteves (University of Pennsylvania) · Ameesh Makadia (Google Research) · Kostas Daniilidis (University of Pennsylvania)

SnapBoost: A Heterogeneous Boosting Machine
Thomas Parnell (IBM Research) · Andreea Anghel (IBM Research) · Małgorzata Łazuka (ETH Zürich) · Nikolas Ioannou (IBM Research) · Sebastian Kurella (ETH Zürich) · Peshal Agarwal (ETH Zürich) · Nikolaos Papandreou (IBM Research Zurich) · Haralampos Pozidis (IBM Research)

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Chao Ma (University of Cambridge) · Sebastian Tschiatschek (Microsoft Research) · Richard E Turner (University of Cambridge) · José Miguel Hernández-Lobato (University of Cambridge) · Cheng Zhang (Microsoft Research, Cambridge, UK)

Minimax Estimation of Conditional Moment Models
Nishanth Dikkala (MIT) · Greg Lewis (Microsoft Research) · Lester Mackey (Microsoft Research) · Vasilis Syrgkanis (Microsoft Research)

Kernel Methods Through the Roof: Handling Billions of Points Efficiently
Giacomo Meanti (Universita' di Genova) · Luigi Carratino (University of Genoa) · Lorenzo Rosasco (University of Genova- MIT - IIT) · Alessandro Rudi (INRIA, Ecole Normale Superieure)

A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm
Gersende Fort (CNRS) · Eric Moulines (Ecole Polytechnique) · Hoi-To Wai (The Chinese University of Hong Kong)

ColdGANs: Taming Language GANs with Cautious Sampling Strategies
Thomas Scialom (reciTAL) · Paul-Alexis Dray (reciTAL) · Sylvain Lamprier (LIP6-UPMC) · Benjamin Piwowarski (LIP6, UPMC / CNRS, Paris, France) · Jacopo Staiano (reciTAL)

Logarithmic Pruning is All You Need
Laurent Orseau (DeepMind) · Marcus Hutter (DeepMind) · Omar Rivasplata (DeepMind & UCL)

SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Didrik Nielsen (DTU Compute) · Priyank Jaini (University of Amsterdam) · Emiel Hoogeboom (University of Amsterdam) · Ole Winther (DTU and KU) · Max Welling (University of Amsterdam / Qualcomm AI Research)

Linear Time Sinkhorn Divergences using Positive Features
Meyer Scetbon (CREST-ENSAE) · Marco Cuturi (Google Brain & CREST - ENSAE)

Applications of Common Entropy for Causal Inference
Murat Kocaoglu (IBM Research) · Sanjay Shakkottai (University of Texas at Austin) · Alexandros Dimakis (University of Texas, Austin) · Constantine Caramanis (UT Austin) · Sriram Vishwanath (University of Texas at Austin)

Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm
Adil SALIM (KAUST) · Peter Richtarik (KAUST)

Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe
Sanghack Lee (Columbia University) · Elias Bareinboim (Columbia University)

Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
Andres Potapczynski (Columbia University) · Gabriel Loaiza-Ganem (Layer 6 AI) · John Cunningham (University of Columbia)

Equivariant Networks for Hierarchical Structures
Renhao Wang (University of British Columbia) · Marjan Albooyeh (University of British Columbia) · Siamak Ravanbakhsh (McGill / MILA)

RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
Fan Lu (Tongji University) · Guang Chen (Tongji University) · Yinlong Liu (Technische Universität München) · Zhongnan Qu (ETH Zurich) · Alois Knoll (Robotics and Embedded Systems)

Unsupervised Text Generation by Search and Learning
Jingjing Li (The Chinese University of Hong Kong) · Zichao Li (Huawei Noah's Ark Lab) · Lili Mou (University of Alberta) · Xin Jiang (Huawei Noah's Ark Lab) · Michael Lyu (CUHK) · Irwin King (Chinese University of Hong Kong)

The Autoencoding Variational Autoencoder
Taylan Cemgil (DeepMind) · Sumedh Ghaisas (DeepMind) · Krishnamurthy Dvijotham (DeepMind) · Sven Gowal (DeepMind) · Pushmeet Kohli (DeepMind)

Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks
Soham De (DeepMind) · Sam Smith (Google Brain)

Joint Policy Search for Multi-agent Collaboration with Incomplete Information
Yuandong Tian (Facebook AI Research) · Qucheng Gong (Facebook AI Research) · Yu Jiang (Facebook AI Research)

Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies
Itai Gat (Technion) · Idan Schwartz (Technion) · Alexander Schwing (University of Illinois at Urbana-Champaign) · Tamir Hazan (Technion)

Neural Execution Engines: Learning to Execute Subroutines
Yujun Yan (University of Michigan) · Kevin Swersky (Google) · Danai Koutra (U Michigan) · Parthasarathy Ranganathan (Google) · Milad Hashemi (Google)

Counterfactual Data Augmentation using Locally Factored Dynamics
Silviu Pitis (University of Toronto) · Elliot Creager (University of Toronto) · Animesh Garg (Univ. of Toronto, Vector Institute, Nvidia)

Convolutional Generation of Textured 3D Meshes
Dario Pavllo (ETH Zurich) · Graham Spinks (KU Leuven) · Thomas Hofmann (ETH Zurich) · Marie-Francine Moens (KU Leuven) · Aurelien Lucchi (ETH Zurich)

Entropic Causal Inference: Identifiability and Finite Sample Results
Spencer Compton (MIT) · Murat Kocaoglu (IBM Research) · Kristjan Greenewald (IBM Research) · Dmitriy Katz (IBM Research)

Universal Steganography and Watermarking: Towards Understanding and Utilizing Deep Hiding
Chaoning Zhang (KAIST) · Philipp Benz (KAIST) · Adil Karjauv (KAIST) · Geng Sun (KAIST) · In Kweon (KAIST)

Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
Didrik Nielsen (DTU Compute) · Ole Winther (DTU and KU)

Optimal Private Median Estimation under Minimal Distributional Assumptions
Christos Tzamos (UW-Madison) · Emmanouil-Vasileios Vlatakis-Gkaragkounis (Columbia University) · Ilias Zadik (NYU)

Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
Stephen Mcaleer (UC Irvine) · J.B. Lanier (University of California Irvine) · Roy Fox (UC Irvine) · Pierre Baldi (UC Irvine)

Beyond Lazy Training for Over-parameterized Tensor Decomposition
Xiang Wang (Duke University) · Chenwei Wu (Duke University) · Jason Lee (Princeton University) · Tengyu Ma (Stanford University) · Rong Ge (Duke University)

Higher-Order Spectral Clustering of Directed Graphs
Valdimar Steinar Ericsson Laenen (FiveAI) · He Sun (School of Informatics, The University of Edinburgh)

R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making
Sergey Shuvaev (Cold Spring Harbor Laboratory) · Sarah Starosta (Washington University in St. Louis) · Duda Kvitsiani (Aarhus University) · Adam Kepecs (Washington University in St. Louis) · Alexei Koulakov (Cold Spring Harbor Laboratory)

Black-Box Optimization with Local Generative Surrogates
Sergey Shirobokov (Imperial College London) · Vladislav Belavin (National Research University Higher School of Economics) · Michael Kagan (SLAC / Stanford) · Andrei Ustyuzhanin (National Research University Higher School of Economics) · Atilim Gunes Baydin (University of Oxford)

A Functional EM Algorithm for Panel Count Data with Missing Counts
Alexander Moreno (Georgia Institute of Technology) · Zhenke Wu (University of Michigan) · Jamie Roslyn Yap (University of Michigan) · Cho Lam (University of Utah) · David Wetter (University of Utah) · Inbal Nahum-Shani (University of Michigan) · Walter Dempsey (University of Michigan) · James M Rehg (Georgia Tech)

Coresets for Robust Training of Deep Neural Networks against Noisy Labels
Baharan Mirzasoleiman (Stanford University) · Kaidi Cao (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

Reliable Graph Neural Networks via Robust Location Estimation
Simon Geisler (Technical University of Munich) · Daniel Zügner (Technical University of Munich) · Stephan Günnemann (Technical University of Munich)

Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals
Jing Shi (Institute of Automation Chinese Academy of Sciences) · Xuankai Chang (Johns Hopkins University) · Pengcheng Guo (Northwestern Polytechnical University) · Shinji Watanabe (Johns Hopkins University) · Yusuke Fujita (Hitachi) · Jiaming Xu (Institute of Automation Chinese Academy of Sciences) · Bo Xu (Institute of Automation, Chinese Academy of Sciences) · Lei Xie (Northwestern Polytechnical University)

Your Classifier can Secretly Suffice Multi-Source Domain Adaptation
Naveen Venkat (Indian Institute of Science) · Jogendra Nath Kundu (Indian Institute of Science) · Durgesh K. Singh (Indian Institute of Science) · Ambareesh Revanur (Indian Institute of Science) · Venkatesh Babu R (Indian institute of science)

Exploiting the Surrogate Gap in Online Multiclass Classification
Dirk van der Hoeven (Leiden University)

Dissecting Neural ODEs
Stefano Massaroli (The University of Tokyo) · Michael Poli (KAIST) · Jinkyoo Park (KAIST) · Atsushi Yamashita (The University of Tokyo) · edit Hajime Asama (The University of Tokyo)

Ensembling geophysical models with Bayesian Neural Networks
Ushnish Sengupta (University of Cambridge) · Matt Amos (Lancaster University) · Scott Hosking (British Antarctic Survey) · Carl Edward Rasmussen (University of Cambridge) · Matthew Juniper (University of Cambridge) · Paul Young (Lancaster University)

Estimating Fluctuations in Neural Representations of Uncertain Environments
Sahand Farhoodi (Boston University) · Mark Plitt (Stanford University) · Lisa Giocomo (Stanford University) · Uri Eden (Boston University)

Novelty Search in representational space for sample efficient exploration
Ruo Yu Tao (University of Alberta) · Vincent Francois-Lavet (McGill) · Joelle Pineau (McGill University)

Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks
Alexander Shekhovtsov (Czech Technical University in Prague, Czech Republic) · Viktor Yanush (Lomonosov Moscow State University) · Boris Flach (Czech Technical University in Prague)

Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets
Avetik Karagulyan (Center for Research in Economics and Statistics / ENSAE / IPP) · Arnak Dalalyan (ENSAE ParisTech)

ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool
Gellert Weisz (Deepmind) · András György (DeepMind) · Wei-I Lin (UBC) · Devon Graham (University of British Columbia) · Kevin Leyton-Brown (University of British Columbia) · Csaba Szepesvari (DeepMind / University of Alberta) · Brendan Lucier (Microsoft Research)

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
Bastian Rieck (ETH Zurich) · Tristan Yates (Yale University) · Christian Bock (ETH Zurich) · Karsten Borgwardt (ETH Zurich) · Guy Wolf (Université de Motréal; Mila) · Nicholas Turk-Browne (Yale University) · Smita Krishnaswamy (Yale University)

Comparator-Adaptive Convex Bandits
Dirk van der Hoeven (Leiden University) · Ashok Cutkosky (Google Research) · Haipeng Luo (University of Southern California)

Hypersolvers: Toward Fast Continuous-Depth Models
Michael Poli (KAIST) · Stefano Massaroli (The University of Tokyo) · Atsushi Yamashita (The University of Tokyo) · edit Hajime Asama (The University of Tokyo) · Jinkyoo Park (KAIST)

Iterative Deep Graph Learning for Graph NeuralNetworks: Better and Robust Node Embeddings
Yu Chen (Facebook) · Lingfei Wu (IBM Research AI) · Mohammed Zaki (RPI)

What went wrong and when? \\ Instance-wise feature importance for time-series black-box models
Sana Tonekaboni (University of Toronto Vector Institute) · Shalmali Joshi (Harvard University (SEAS)) · Kieran Campbell (University of British Columbia) · David Duvenaud (University of Toronto) · Anna Goldenberg ()

STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks
Meiyi Ma (University of Virginia) · Ji Gao (University of Virginia) · Lu Feng (University of Virginia) · John A Stankovic (University of Virginia)

Rescuing neural spike train models from bad MLE
Diego Arribas (Stony Brook University) · Yuan Zhao (Stony Brook University) · Il Memming Park (Stony Brook University)

Multi-agent active perception with prediction rewards
Mikko Lauri (University of Hamburg) · Frans Oliehoek (TU Delft)

The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise
Ilias Diakonikolas (UW Madison) · Daniel M. Kane (UCSD) · Pasin Manurangsi (Google)

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning
Ziyu Wang (Deepmind) · Caglar Gulcehre (Deepmind) · Alexander Novikov (DeepMind) · Thomas Paine (DeepMind) · Sergio Gómez (DeepMind) · Konrad Zolna (DeepMind) · Rishabh Agarwal (Google Research, Brain Team) · Josh Merel (DeepMind) · Daniel Mankowitz (DeepMind) · Cosmin Paduraru (DeepMind) · Gabriel Dulac-Arnold (Google Research) · Jerry Li (Google) · Mohammad Norouzi (Google Brain) · Matthew Hoffman (DeepMind) · Nicolas Heess (Google DeepMind) · Nando de Freitas (DeepMind)

High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds
Noémie Jaquier (Karlsruhe Institute of Technology) · Leonel Rozo (Bosch Center for Artificial Intelligence)

A local temporal difference code for distributional reinforcement learning
Pablo Tano (University of Geneva) · Peter Dayan (Max Planck Institute for Biological Cybernetics) · Alexandre Pouget (University of Geneva)

Synbols: Probing Learning Algorithms with Synthetic Datasets
Alexandre Lacoste (Element AI) · Pau Rodríguez López (CVC UAB) · Frederic Branchaud-Charron (Element AI) · Parmida Atighehchian (ElementAI) · Massimo Caccia (MILA) · Issam Hadj Laradji (University of British Columbia) · Alexandre Drouin (Element AI) · Matthew Craddock (Element AI) · Laurent Charlin (MILA / U.Montreal) · David Vázquez (Element AI)

Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Thomas Anthony (DeepMind) · Tom Eccles (DeepMind) · Andrea Tacchetti (DeepMind) · János Kramár (DeepMind) · Ian Gemp (DeepMind) · Thomas Hudson (DeepMind) · Nicolas Porcel (DeepMind) · Marc Lanctot (DeepMind) · Julien Perolat (DeepMind) · Richard Everett (DeepMind) · Satinder Singh (DeepMind) · Thore Graepel (DeepMind) · Yoram Bachrach ()

Forethought and Hindsight in Credit Assignment
Veronica Chelu (McGill University) · Doina Precup (McGill University / Mila / DeepMind Montreal) · Hado van Hasselt (DeepMind)

When Counterpoint Meets Chinese Folk Melodies
Nan Jiang (Tsinghua University) · Sheng Jin (Tsinghua University) · Zhiyao Duan (Unversity of Rochester) · Changshui Zhang (Tsinghua University)

Escaping the Gravitational Pull of Softmax
Jincheng Mei (University of Alberta / Google Brain) · Chenjun Xiao (University of Alberta) · Bo Dai (Google Brain) · Lihong Li (Google Research) · Csaba Szepesvari (DeepMind / University of Alberta) · Dale Schuurmans (Google Brain & University of Alberta)

Diverse Image Captioning with Context-Object Split Latent Spaces
Shweta Mahajan (TU Darmstadt) · Stefan Roth (TU Darmstadt)

Organizing recurrent network dynamics by task-computation to enable continual learning
Lea Duncker (Gatsby Unit, UCL) · Laura N Driscoll (Stanford) · Krishna V Shenoy (Stanford University) · Maneesh Sahani (Gatsby Unit, UCL) · David Sussillo (Stanford University)

The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification
Christian Tjandraatmadja (Google) · Ross Anderson (Google Research) · Joey Huchette (Rice University) · Will Ma (Columbia University) · KRUNAL KISHOR PATEL (Google) · Juan Pablo Vielma (Google and MIT)

Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
Dmitry Kovalev (KAUST) · Adil SALIM (KAUST) · Peter Richtarik (KAUST)

Quantized Variational Inference
Amir Dib (ENS Paris-Saclay, Université Paris-Saclay)

The Value Equivalence Principle for Model-Based Reinforcement Learning
Christopher Grimm (University of Michigan) · Andre Barreto (DeepMind) · Satinder Singh (DeepMind) · David Silver (DeepMind)

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
Anqi Wu () · E. Kelly Buchanan (Columbia University) · Matthew Whiteway (Columbia University) · Michael Schartner (University of Geneva) · Guido Meijer (Champalimaud Center for the Unknown) · Jean-Paul Noel (New York University) · Erica Rodriguez (Columbia University) · Claire Everett (Columbia University) · Amy Norovich (Columbia University) · Evan Schaffer (Columbia University) · Neeli Mishra (Columbia University) · C. Daniel Salzman (Columbia University) · Dora Angelaki (New York University) · Andrés Bendesky (Columbia University) · The International Brain Laboratory The International Brain Laboratory (The International Brain Laboratory) · John Cunningham (University of Columbia) · Liam Paninski (Columbia University)

UCLID-Net: Single View Reconstruction in Object Space
Benoit Guillard (EPFL) · Edoardo Remelli (EPFL) · Pascal Fua (EPFL, Switzerland)

Unsupervised Sound Separation Using Mixtures of Mixtures
Scott Wisdom (Google) · Efthymios Tzinis (University of Illinois at Urbana-Champaign) · Hakan Erdogan (Google) · Ron Weiss (Google) · Kevin Wilson (Google) · John Hershey (Google)

Exploiting weakly supervised visual patterns to learn from partial annotations
Kaustav Kundu (Amazon) · Joseph Tighe (Amazon)

Efficient semidefinite-programming-based inference for binary and multi-class MRFs
Chirag Pabbaraju (Carnegie Mellon University) · Po-Wei Wang (CMU) · J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

Measuring Systematic Generalization in Neural Proof Generation with Transformers
Nicolas Gontier (Mila, Polytechnique Montréal) · Koustuv Sinha (McGill University / Mila / FAIR) · Siva Reddy (McGill University) · Chris Pal (Montreal Institute for Learning Algorithms, École Polytechnique, Université de Montréal)

Directional convergence and alignment in deep learning
Ziwei Ji (University of Illinois Urbana-Champaign) · Matus Telgarsky (UIUC)

Finer Metagenomic Reconstruction via Biodiversity Optimization
Simon Foucart (Texas A&M) · David Koslicki (Pennsylvania State University)

Monotone operator equilibrium networks
Ezra Winston (Carnegie Mellon University) · J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
Tiziano Portenier (ETH Zurich) · Siavash Arjomand Bigdeli (CSEM) · Orcun Goksel (ETH Zurich)

Decentralized Langevin Dynamics for Bayesian Learning
Anjaly Parayil (Postdoctoral Associate, Army Research Laboratory ) · He Bai (Oklahoma State University) · Jemin George (Army Research Laboratory) · Prudhvi Gurram (Booz Allen Hamilton)

Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Thomas M Sutter (ETH Zurich) · Imant Daunhawer (ETH Zurich) · Julia Vogt (ETH Zurich)

Linear Dynamical Systems as a Core Computational Primitive
Shiva Kaul (Carnegie Mellon University)

Avoiding Side Effects By Considering Future Tasks
Victoria Krakovna (DeepMind) · Laurent Orseau (DeepMind) · Richard Ngo (DeepMind) · Miljan Martic (DeepMind) · Shane Legg (DeepMind)

Multi-agent Trajectory Prediction with Fuzzy Query Attention
Nitin Kamra (University of Southern California) · Hao Zhu (Peking University) · Dweep Kumarbhai Trivedi (University of Southern California) · Ming Zhang (Peking University) · Yan Liu (University of Southern California)

Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality
Yi Zhang (Princeton University) · Orestis Plevrakis (Princeton University) · Simon Du (Institute for Advanced Study) · Xingguo Li (Princeton University) · Zhao Song (IAS/Princeton) · Sanjeev Arora (Princeton University)

Trust the Model When It Is Confident: Masked Model-based Actor-Critic
Feiyang Pan (Institute of Computing Technology, Chinese Academy of Sciences) · Jia He (Huawei) · Dandan Tu (Huawei) · Qing He (Institute of Computing Technology, Chinese Academy of Sciences)

POMDPs in Continuous Time and Discrete Spaces
Bastian Alt (Technische Universität Darmstadt) · Matthias Schultheis (Technische Universität Darmstadt) · Heinz Koeppl (Technische Universität Darmstadt)

Goal-directed Generation of Discrete Structures with Conditional Generative Models
Maolaaisha Aminanmu (University of Geneva,University of Applied Sciences Western Switzerland) · Brooks Paige (University College London) · Alexandros Kalousis (University of Applied Sciences, Western Switzerland)

Online MAP Inference of Determinantal Point Processes
Aditya Bhaskara (University of Utah) · Amin Karbasi (Yale) · Silvio Lattanzi (Google Research) · Morteza Zadimoghaddam (Google Research)

Steady State Analysis of Episodic Reinforcement Learning
Huang Bojun (Rakuten Institute of Technology)

Learning Feature Sparse Principal Subspace
Lai Tian (Northwestern Polytechnical University) · Feiping Nie (University of Texas Arlington) · Rong Wang (Northwestern Polytechnical University) · Xuelong Li (Northwestern Polytechnical Univ.)

Learning Multi-Agent Communication through Structured Attentive Reasoning
Murtaza Rangwala (Virginia Tech) · Ryan K Williams (Virginia Tech)

Early-Learning Regularization Prevents Memorization of Noisy Labels
Sheng Liu (NYU) · Jonathan Niles-Weed (NYU) · Narges Razavian (New York University School of Medicine) · Carlos Fernandez-Granda (NYU)

Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Tom Heskes (Radboud University Nijmegen) · Evi Sijben (Radboud University) · Ioan Gabriel Bucur (Radboud University Nijmegen) · Tom Claassen (Radboud University Nijmegen)

Information-theoretic Task Selection for Meta-Reinforcement Learning
Ricardo Luna Gutierrez (University of Leeds) · Matteo Leonetti (University of Leeds)

Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier
Zhenwei Dai (Rice University) · Anshumali Shrivastava (Rice University)

Normalizing Kalman Filters for Multivariate Time Series Analysis
Emmanuel de Bézenac (Sorbonne Université) · Syama Sundar Rangapuram (Amazon Research) · Konstantinos Benidis (Amazon Research) · Michael Bohlke-Schneider (Amazon) · Lorenzo Stella (Amazon Research) · Hilaf Hasson (Amazon Research) · Richard Kurle (Volkswagen Group) · Tim Januschowski (Amazon Research) · Patrick Gallinari (Sorbonne University & Criteo AI Lab, Paris)

Active Structure Learning of Causal DAGs via Directed Clique Trees
Chandler Squires (Massachusetts Institute of Technology) · Sara Magliacane (MIT-IBM Watson AI Lab, IBM Research) · Kristjan Greenewald (IBM Research) · Dmitriy Katz (IBM Research) · Murat Kocaoglu (IBM Research) · Karthikeyan Shanmugam (IBM Research, NY)

EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints
Sachin Chauhan (IIT-Delhi) · Kashish Bansal (IIT Delhi) · Rijurekha Sen (IIT DELHI)

Collapsing Bandits and Their Application to Public Health Intervention
Aditya Mate (Harvard University) · Jackson Killian (Harvard University) · Haifeng Xu (University of Virginia) · Andrew Perrault (Harvard University) · Milind Tambe (Harvard University/Google)

Consistent Plug-in Classifiers for Complex Objectives and Constraints
Shiv Kumar Tavker (IIT Madras) · Harish Guruprasad Ramaswamy (IIT Madras) · Harikrishna Narasimhan (Google Research)

An Optimal Elimination Algorithm for Learning a Best Arm
Avinatan Hassidim (Google) · Ron Kupfer (The Hebrew University of Jerusalem) · Yaron Singer (Harvard University)

Delta-STN: Efficient Bilevel Optimization of Neural Networks using Structured Response Jacobians
Juhan Bae (University of Toronto) · Roger Grosse (University of Toronto)

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Matthew Tancik (UC Berkeley) · Pratul Srinivasan (UC Berkeley) · Ben Mildenhall (UC Berkeley) · Sara Fridovich-Keil (UC Berkeley) · Nithin Raghavan (UC Berkeley) · Utkarsh Singhal (UC Berkeley) · Ravi Ramamoorthi (University of California San Diego) · Jonathan Barron (Google Research) · Ren Ng (University of California, Berkeley)

A Simple and Efficient Smoothing Method for Accelerated Optimization and Local Exploration
Kevin Scaman (Noah's Ark Lab, Huawei Technologies) · Ludovic DOS SANTOS (Huawei) · Merwan Barlier (Huawei Technologies) · Igor Colin (Huawei)

Online Algorithm for Unsupervised Sequential Selection with Contextual Information
Arun Verma (Indian Institute of Technology Bombay) · Manjesh Kumar Hanawal (IIT Bombay) · Csaba Szepesvari (DeepMind / University of Alberta) · Venkatesh Saligrama (Boston University)

Calibrating Deep Neural Networks using Focal Loss
Jishnu Mukhoti (University of Oxford) · Viveka Kulharia (University of Oxford) · Amartya Sanyal (University of Oxford) · Stuart Golodetz (FiveAI Ltd.) · Philip Torr (University of Oxford) · Puneet Dokania (University of Oxford)

Modular Meta-Learning with Shrinkage
Yutian Chen (DeepMind) · Abram Friesen (DeepMind) · Feryal Behbahani (DeepMind) · Arnaud Doucet (Google DeepMind) · David Budden (DeepMind) · Matthew Hoffman (DeepMind) · Nando de Freitas (DeepMind)

ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Chia-Yu Chen (IBM research) · Jiamin Ni (IBM) · Songtao Lu (IBM) · Xiaodong Cui (IBM T. J. Watson Research Center) · Pin-Yu Chen (IBM Research AI) · Xiao Sun (IBM Thomas J. Watson Research Center) · Naigang Wang (IBM T. J. Watson Research Center) · Swagath Venkataramani (IBM Research) · Vijayalakshmi (Viji) Srinivasan (IBM TJ Watson) · Wei Zhang (IBM T.J.Watson Research Center) · Kailash Gopalakrishnan (IBM Research)

Towards practical differentially private causal graph discovery
Lun Wang (University of California, Berkeley) · Qi Pang (The Hong Kong University of Science and Technology) · Dawn Song (UC Berkeley)

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
Yue Wu (Carnegie Mellon University) · Pan Zhou (National University of Singapore) · Andrew Gordon Wilson (New York University) · Eric Xing (Petuum Inc. / Carnegie Mellon University) · Zhiting Hu (Carnegie Mellon University)

Top-KAST: Top-K Always Sparse Training
Siddhant Jayakumar (Google DeepMind) · Razvan Pascanu (Google DeepMind) · Jack Rae (DeepMind, UCL) · Simon Osindero (DeepMind) · Erich Elsen (DeepMind)

Model Selection in Contextual Stochastic Bandit Problems
Aldo Pacchiano (UC Berkeley) · My Phan (University of Massachusetts Amherst) · Yasin Abbasi Yadkori (VinAI Research/ VinTech JSC.,) · Anup Rao (School of Computer Science, Georgia Tech) · Julian Zimmert (Google) · Tor Lattimore (DeepMind) · Csaba Szepesvari (DeepMind / University of Alberta)

Simultaneous Preference and Metric Learning from Paired Comparisons
Austin Xu (Georgia Institute of Technology) · Mark Davenport (Georgia Institute of Technology)

Unsupervised Continuous Object Representation Networks for Novel View Synthesis
Nicolai Hani (University of Minnesota) · Selim Engin (University of Minnesota) · Jun-Jee Chao (University of Minnesota) · Volkan Isler (University of Minnesota, Twin Cities)

Disentangling Human Error from Ground Truth in Segmentation of Medical Images
Le Zhang (University College London) · Ryutaro Tanno (Microsoft Research / UCL) · Moucheng Xu (University College London) · Chen Jin (University College London) · Joseph Jacob (University College London) · Olga Cicarrelli (Queen Square Multiple Sclerosis Centre) · Frederik Barkhof (University College London) · Daniel Alexander (University College London)

Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization
Hung-Jen Chen (National Tsing Hua University) · An-Chieh Cheng (National Tsing Hua University) · Da-Cheng Juan (Google) · Wei Wei (CMU) · Min Sun (Appier, Inc.)

Inferring learning rules from animal decision-making
Zoe Ashwood (Princeton University) · Nicholas A Roy (Princeton Neuroscience Institute) · Ji Hyun Bak (UC Berkeley) · Jonathan W Pillow (Princeton University)

Learning Differential Equations that are Fast to Solve
Jacob Kelly (University of Toronto) · Jesse Bettencourt (University of Toronto) · Matthew Johnson (Google Brain) · David Duvenaud (University of Toronto)

Convolutional Tensor-Train LSTM for Spatio-Temporal Learning
Jiahao Su (University of Maryland) · Wonmin Byeon (NVIDIA Research) · Jean Kossaifi (NVIDIA) · Furong Huang (University of Maryland) · Jan Kautz (NVIDIA) · Anima Anandkumar (NVIDIA / Caltech)

The Mean-Squared Error of Double Q-Learning
Wentao Weng (Tsinghua University) · Harsh Gupta (University of Illinois at Urbana-Champaign) · Niao He (UIUC) · Lei Ying (University of Michigan) · R. Srikant (University of Illinois at Urbana-Champaign)

Temporal Variability in Implicit Online Learning
Nicolò Campolongo (Università degli Studi di Milano) · Francesco Orabona (Boston University)

Instance Selection for GANs
Terrance DeVries (University of Guelph) · Michal Drozdzal (FAIR) · Graham W Taylor (University of Guelph)

An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits
Julian Katz-Samuels () · Lalit Jain (University of Washington) · zohar karnin (Amazon) · Kevin Jamieson (U Washington)

Neural Topographic Factor Analysis for fMRI Data
Eli Sennesh (Northeastern University) · Zulqarnain Khan (Northeastern University) · Yiyu Wang (Northeastern University) · J Benjamin Hutchinson (University of Oregon) · Ajay Satpute (Northeastern) · Jennifer Dy (Northeastern University) · Jan-Willem van de Meent (Northeastern University)

Sample complexity and effective dimension for regression on manifolds
Andrew McRae (Georgia Institute of Technology) · Justin Romberg (Georgia Institute of Technology) · Mark Davenport (Georgia Institute of Technology)

Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations
Zhe Zeng (University of California, Los Angeles) · Paolo Morettin (University of Trento) · Fanqi Yan (University of California, Los Angeles) · Antonio Vergari (University of California, Los Angeles) · Guy Van den Broeck (UCLA)

Compositional recognition with causally-driven embeddings
Yuval Atzmon (NVIDIA Research) · Felix Kreuk (Bar-Ilan University) · Uri Shalit (Technion) · Gal Chechik (NVIDIA, BIU)

Learning discrete distributions: user vs item-level privacy
Yuhan Liu (Cornell University) · Ananda Theertha Suresh (Google) · Felix Xinnan Yu (Google Research) · Sanjiv Kumar (Google Research) · Michael D Riley (Google)

Limits on Testing Structural Changes in Ising Models
Aditya Gangrade (Boston University) · Bobak Nazer (Boston University) · Venkatesh Saligrama (Boston University)

Almost Surely Stable Deep Dynamics
Nathan Lawrence (University of British Columbia) · Philip Loewen (University of British Columbia) · Michael Forbes (Honeywell) · Johan Backstrom (Honeywell) · Bhushan Gopaluni (University of British Columbia)

Towards Learning Convolutions from Scratch
Behnam Neyshabur (Google)

Bayesian Bits: Unifying Quantization and Pruning
Mart van Baalen (Qualcomm) · Christos Louizos (Qualcomm AI Research) · Markus Nagel (Qualcomm) · Rana Ali Amjad (Qualcomm) · Ying Wang (Qualcomm) · Tijmen Blankevoort (Qualcomm) · Max Welling (University of Amsterdam / Qualcomm AI Research)

Neural FFTs for Universal Texture Image Synthesis
Morteza Mardani (NVIDIA) · Guilin Liu (NVIDIA) · Aysegul Dundar (NVIDIA) · Shiqiu Liu (NVIDIA) · Andrew Tao (Nvidia Corporation) · Bryan Catanzaro (NVIDIA)

A Simple Language Model for Task-Oriented Dialogue
Ehsan Hosseini-Asl (Salesforce Research) · Bryan McCann (Salesforce Research) · Chien-Sheng Wu (Salesforce Research) · Semih Yavuz (Salesforce) · Richard Socher (Salesforce)

The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks
Wei Hu (Princeton University) · Lechao Xiao (Google Brain) · Ben Adlam (Google) · Jeffrey Pennington (Google Brain)

A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
Xiaotie Deng (Peking University) · Ron Lavi (Technion) · Tao Lin (Peking University) · Qi Qi (Hong Kong University of Science and Technology) · Wenwei WANG (Alibaba Group) · Xiang Yan (Shanghai Jiao Tong University)

Understanding spiking networks through convex optimization
Allan Mancoo (Champalimaud Centre for the Unknown) · Sander Keemink (Champalimaud Centre for the Unknown) · Christian K Machens (Champalimaud Centre for the Unknown)

Projected Stein Variational Gradient Descent
Peng Chen (The University of Texas at Austin) · Omar Ghattas (The University of Texas at Austin)

PEP: Parameter Ensembling by Perturbation
Alireza Mehrtash (University of British Columbia) · Purang Abolmaesumi (UBC) · Polina Golland (Massachusetts Institute of Technology) · Tina Kapur (Brigham and Women's Hospital) · Demian Wassermann (Inria) · William Wells (Harvard Medical School)

Tiny Transfer Learning: Towards Memory-Efficient On-Device Learning
Han Cai (Massachusetts Institute of Technology) · Chuang Gan (MIT-IBM Watson AI Lab) · Ligeng Zhu (MIT) · Song Han (MIT)

SVGD as a kernelized gradient flow of the chi-squared divergence
Sinho Chewi (Massachusetts Institute of Technology) · Thibaut Le Gouic (Massachusetts Institute of Technology) · Chen Lu (Massachusetts Institute of Technology) · Tyler Maunu (Massachusetts Institute of Technology) · Philippe Rigollet (MIT)

Provable, Scalable and Automatic Perturbation Analysis on General Computational Graphs
Kaidi Xu (Northeastern University) · Zhouxing Shi (UCLA) · Huan Zhang (UCLA) · Yihan Wang (JD.com) · Kai-Wei Chang (UCLA) · Minlie Huang (Tsinghua University) · Bhavya Kailkhura (Lawrence Livermore National Lab) · Xue Lin (Northeastern University) · Cho-Jui Hsieh (UCLA)

Learning to summarize with human feedback
Nisan Stiennon (OpenAI) · Long Ouyang (OpenAI) · Jeffrey Wu (OpenAI) · Daniel Ziegler (OpenAI) · Ryan Lowe (McGill University / OpenAI) · Chelsea Voss (OpenAI) · Alec Radford (OpenAI) · Dario Amodei (OpenAI) · Paul Christiano (OpenAI)

Provable Worst Case Guarantees for the Detection of Out-of-distribution Data
Julian Bitterwolf (University of Tübingen) · Alexander Meinke (University of Tübingen) · Matthias Hein (University of Tübingen)

Interpolation technique to speed up gradients propagation in Neural Ordinary Differential Equations
Talgat Daulbaev (Skolkovo Institute of Science and Technology) · Alexandr Katrutsa (Skolkovo Institute of Science and Technology) · Larisa Markeeva (Skolkovo Institute of Science and Technology) · Julia Gusak (Skolkovo Institute of Science and Technology) · Andrzej Cichocki (Skolkovo Institute of Science and Technology) · Ivan Oseledets (Skoltech)

A Unifying View of Optimism in Episodic Reinforcement Learning
Gergely Neu (Universitat Pompeu Fabra) · Ciara Pike-Burke (Universitat Pomepu Fabra)

Asymptotic Guarantees for Generative Modeling based on the Smooth Wasserstein Distance
Ziv Goldfeld (Cornell University) · Kristjan Greenewald (IBM Research) · Kengo Kato (Cornell University)

Estimating weighted areas under the ROC curve
Andreas Maurer () · Massimiliano Pontil (IIT & UCL)

Preference learning along multiple criteria: A game-theoretic perspective
Kush Bhatia (UC Berkeley) · Ashwin Pananjady (UC Berkeley) · Peter Bartlett (UC Berkeley) · Anca Dragan (UC Berkeley) · Martin Wainwright (UC Berkeley)

CompReSS: Compressing Representations for Self-Supervised Learning
Soroush Abbasi Koohpayegani (University of Maryland Baltimore County) · Ajinkya Tejankar (University of Maryland Baltimore County) · Hamed Pirsiavash (University of Maryland, Baltimore County)

GAMA: Guided Adversarial Margin Attack
Gaurang Sriramanan (Indian Institute of Science, Bangalore) · Sravanti Addepalli (Indian Institute of Science) · Arya Baburaj (Indian Institute of Science) · Venkatesh Babu R (Indian institute of science)

Adaptive Learning for Fast Adaptation
Sungyong Baik (Seoul National University) · Myungsub Choi (Seoul National University) · Janghoon Choi (Seoul National University) · Heewon Kim (Seoul National University) · Kyoung Mu Lee (Seoul National University)

Accelerating Reinforcement Learning through GPU Atari Emulation
Steven Dalton (Nvidia) · iuri frosio (nvidia)

SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology
Siddharth Samsi (MIT Lincoln Laboratory) · Mark Veillette (MIT Lincoln Laboratory) · Chris Mattioli (MIT Lincoln Laboratory)

Object Goal Navigation using Semantically Aware Exploration
Devendra Singh Chaplot (Carnegie Mellon University) · Dhiraj Prakashchand Gandhi (Carnegie Mellon University) · Abhinav Gupta (Facebook AI Research/CMU) · Russ Salakhutdinov (Carnegie Mellon University)

Probably Approximately Correct Constrained Learning
Luiz Chamon (University of Pennsylvania) · Alejandro Ribeiro (University of Pennsylvania)

Online learning with dynamics: A minimax perspective
Kush Bhatia (UC Berkeley) · Karthik Sridharan (Cornell University)

Learning to Approximate a Bregman Divergence
Ali Siahkamari (Boston University) · XIDE XIA (Boston University) · Venkatesh Saligrama (Boston University) · David Castañón (Boston University) · Brian Kulis (Boston University and Amazon)

Deep Subspace Clustering with Data Augmentation
Mahdi Abavisani (Rutgers, The State University of New Jersey) · Alireza Naghizadeh (Rutgers University) · Dimitris Metaxas (Rutgers University) · Vishal Patel (Johns Hopkins University)

Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
Samarth Sinha (University of Toronto, Vector Institute) · Zhengli Zhao (UCI, Google Brain) · Anirudh Goyal ALIAS PARTH GOYAL (Université de Montréal) · Colin A Raffel (Google Brain) · Augustus Odena (Google Brain)

Point process models for sequence detection in high-dimensional neural spike trains
Alex H Williams (Stanford University) · Anthony Degleris (Stanford University) · Yixin Wang (Columbia University) · Scott Linderman (Stanford University)

Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity
Shuxiao Chen (University of Pennsylvania) · Hangfeng He (University of Pennsylvania) · Weijie Su (The Wharton School, University of Pennsylvania)

Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
Mohammadreza Mousavi Kalan (University of Southern California) · Zalan Fabian (University of Southern California) · Salman Avestimehr (University of Southern California) · Mahdi Soltanolkotabi (University of Southern california)

Self-supervised learning through the eyes of a child
Emin Orhan (New York University) · Vaibhav Gupta (New York University) · Brenden Lake (New York University)

NeuMiss networks: differentiable programming for supervised learning with missing values.
Marine Le Morvan (INRIA) · Julie Josses (CMAP / CNRS) · Thomas Moreau (Inria) · Erwan Scornet (Ecole Polytechnique) · Gael Varoquaux (Parietal Team, INRIA)

Deep Evidential Regression
Alexander Amini (MIT) · Wilko Schwarting (Massachusetts Institute of Technology) · Ava Soleimany (MIT) · Daniela Rus (Massachusetts Institute of Technology)

JAX MD: A Framework for Differentiable Physics
Samuel Schoenholz (Google Brain) · Ekin Dogus Cubuk (Google Brain)

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
Alexei Baevski (Facebook AI Research) · Yuhao Zhou (University of Toronto) · Abdel-rahman Mohamed (Facebook AI Research (FAIR)) · Michael Auli (Facebook AI Research)

Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
Steven Jecmen (Carnegie Mellon University) · Hanrui Zhang (Duke University) · Ryan Liu (Carnegie Mellon University) · Nihar Shah (CMU) · Vincent Conitzer (Duke University) · Fei Fang (Carnegie Mellon University)

A convex optimization formulation for multivariate regression
Yunzhang Zhu (Ohio State University)

Confidence sequences for sampling without replacement
Ian Waudby-Smith (Carnegie Mellon University) · Aaditya Ramdas (CMU)

On the distance between two neural networks and the stability of learning
Jeremy Bernstein (Caltech) · Arash Vahdat (NVIDIA) · Yisong Yue (Caltech) · Ming-Yu Liu (Nvidia Research)

Efficient Topological Layer based on Persistent Landscapes
Kwangho Kim (Carnegie Mellon University) · Jisu Kim (Inria Saclay) · Manzil Zaheer (Google Research) · Joon Kim (Carnegie Mellon University) · Frederic Chazal (INRIA) · Larry Wasserman (Carnegie Mellon University)

Limits to Depth Efficiencies of Self-Attention
Yoav Levine (HUJI) · Noam Wies (Hebrew University of Jerusalem) · Or Sharir (Hebrew University of Jerusalem) · Hofit Bata (Hebrew University of Jerusalem) · Amnon Shashua (Hebrew University of Jerusalem)

User-Dependent Neural Sequence Models for Continuous-Time Event Data
Alex Boyd (UC Irvine) · Robert Bamler (University of California at Irvine) · Stephan Mandt (University of California, Irivine) · Padhraic Smyth (University of California, Irvine)

A Discrete Variational Recurrent Topic Model without the Reparametrization Trick
Mehdi Rezaee (University of Maryland Baltimore County) · Francis Ferraro (University of Maryland Baltimore County)

Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment
Govinda Kamath (Microsoft Research) · Tavor Baharav (Stanford University) · Ilan Shomorony (University of Illinois at Urbana Champaign)

Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics
Alex Tseng (Stanford University) · Avanti Shrikumar (Stanford University) · Anshul Kundaje (Stanford University)

Latent Bandits Revisited
Joey Hong (Google AI) · Manzil Zaheer (Google Research) · Yinlam Chow (Google Research) · Branislav Kveton (Google Research) · Amr Ahmed (Google Research) · Craig Boutilier (Google)

Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control
Yaofeng Desmond Zhong (Princeton University) · Naomi Leonard (Princeton University)

Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
Sirisha Rambhatla (University of Southern California) · Xingguo Li (Princeton University) · Jarvis Haupt (University of Minnesota)

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
Huan Zhang (UCLA) · Hongge Chen (MIT) · Chaowei Xiao (University of Michigan, Ann Arbor) · Bo Li (UIUC) · mingyan liu (university of Michigan, Ann Arbor) · Duane Boning (Massachusetts Institute of Technology) · Cho-Jui Hsieh (UCLA)

Recurrent Quantum Neural Networks
Johannes Bausch (University of Cambridge)

Axioms for Learning from Pairwise Comparisons
Ritesh Noothigattu (Carnegie Mellon University) · Dominik Peters (Carnegie Mellon University) · Ariel Procaccia (Harvard University)

Big Bird: Bert for Longer Sequences
Manzil Zaheer (Google Research) · Guru Guruganesh (Google Research) · Kumar Avinava Dubey (Carnegie Mellon University) · Joshua Ainslie (Google) · Chris Alberti (Google) · Santiago Ontanon (Google LLC) · Philip Pham (Google) · Anirudh Ravula (Google) · Qifan Wang (Google Research) · Li Yang (Google) · Amr Ahmed (Google Research)

What is being transferred in transfer learning?
Behnam Neyshabur (Google) · Hanie Sedghi (Google Brain) · Chiyuan Zhang (Google Brain)

Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning
Guangxiang Zhu (Tsinghua university) · Minghao Zhang (Tsinghua University) · Honglak Lee (Google / U. Michigan) · Chongjie Zhang (Tsinghua University)

Generative causal explanations of black-box classifiers
Matthew O'Shaughnessy (Georgia Tech) · Gregory Canal (Georgia Institute of Technology) · Marissa Connor (Georgia Tech) · Christopher Rozell (Georgia Institute of Technology) · Mark Davenport (Georgia Institute of Technology)

The Wasserstein Proximal Gradient Algorithm
Adil SALIM (KAUST) · Anna Korba (Gatsby Unit - UCL) · Giulia Luise (University College London)

Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses
Ronald (James) Cotton (Shirley Ryan AbilityLab) · Fabian Sinz (University Tübingen) · Andreas Tolias (Baylor College of Medicine)

Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases
Senthil Purushwalkam Shiva Prakash (Carnegie Mellon University) · Abhinav Gupta (Facebook AI Research/CMU)

Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests
Sean Kulinski (Purdue University) · Saurabh Bagchi (Purdue University) · David Inouye (Purdue University)

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Rasool Fakoor (Amazon AWS) · Jonas Mueller (Amazon Web Services) · Nick Erickson (Amazon Web Services) · Pratik Chaudhari (University of Pennsylvania) · Alexander Smola (Amazon - We are hiring!)

The Unreasonable Effectiveness of Big Models for Semi-Supervised Learning
Ting Chen (Google) · Simon Kornblith (Google Brain) · Kevin Swersky (Google) · Mohammad Norouzi (Google Brain) · Geoffrey E Hinton (Google & University of Toronto)

Compositional Explanations of Neurons
Jesse Mu (Stanford University) · Jacob Andreas (MIT)

End-to-End Learning and Intervention in Games
Jiayang Li (Northwestern University) · Jing Yu (Northwestern University) · Yu Nie (Northwestern University) · Zhaoran Wang (Northwestern University)

Actionable Recourse Summaries: Uncovering Biases and Disparities in Recourse
Kaivalya Rawal (Harvard University) · Himabindu Lakkaraju (Harvard)

Learning Continuous System Dynamics fromIrregularly-Sampled Partial Observations
Zijie Huang (University of California, Los Angeles) · Yizhou Sun (UCLA) · Wei Wang (UCLA)

Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
Arun Ganesh (University of California Berkeley) · Kunal Talwar (Apple)

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Guy Lorberbom (Technion) · Chris J. Maddison (University of Toronto) · Nicolas Heess (Google DeepMind) · Tamir Hazan (Technion) · Daniel Tarlow (Google Brain)

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation
Charles Margossian (Columbia) · Aki Vehtari (Aalto University) · Daniel Simpson (University of Toronto) · Raj Agrawal (MIT)

Look-ahead Meta Learning for Continual Learning
Gunshi Gupta (University of montreal) · Karmesh Yadav (Carnegie) · Liam Paull (Université de Montréal)

Low Distortion Block-Resampling with Spatially Stochastic Networks
Martin Arjovsky (New York University) · Darryl Barnhart (Latent Space) · Ian Thompson (Latent Space) · Sarah Hong (Latent Space)

Greedy inference with structure-exploiting lazy maps
Michael C Brennan (Massachusetts Institute of Technology) · Daniele Bigoni (Massachusetts Institute of Technology) · Olivier Zahm (INRIA) · Alessio Spantini (Massachusetts Institute of Technology) · Youssef Marzouk (Massachusetts Institute of Technology)

Critic Regularized Regression
Ziyu Wang (Deepmind) · Alexander Novikov (DeepMind) · Konrad Zolna (DeepMind) · Josh Merel (DeepMind) · Jost Tobias Springenberg (DeepMind) · Scott Reed (Google DeepMind) · Bobak Shahriari (Deepmind) · Noah Siegel (DeepMind) · Caglar Gulcehre (DeepMind) · Nicolas Heess (Google DeepMind) · Nando de Freitas (DeepMind)

De-Anonymizing Text by Fingerprinting Language Generation
Zhen Sun (Cornell University) · Roei Schuster (Cornell Tech) · Vitaly Shmatikov (Cornell University)

A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms
Niao He (UIUC) · Donghwan Lee (KAIST)

Adaptive Discretization for Model-Based Reinforcement Learning
Sean Sinclair (Cornell University) · Tianyu Wang (Duke University) · Gauri Jain (Cornell University) · Siddhartha Banerjee (Cornell University) · Christina Yu (Cornell University)

Optimal Best-arm Identification in Linear Bandits
Yassir Jedra (KTH) · Alexandre Proutiere (KTH)

Learning compositional functions via multiplicative weight updates
Jeremy Bernstein (Caltech) · Jiawei Zhao (Caltech) · Markus Meister (Caltech) · Ming-Yu Liu (NVIDIA) · Anima Anandkumar (NVIDIA / Caltech) · Yisong Yue (Caltech)

Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes
Yuval Emek (Technion - Israel Institute of Technology) · Ron Lavi (Technion) · Rad Niazadeh (Chicago Booth School of Business) · Yangguang Shi (Technion - Israel Institute of Technology)

A/B Testing in Dense Large-Scale Networks: Design and Inference
Preetam Nandy (LinkedIn Corporation) · Kinjal Basu (LinkedIn) · Shaunak Chatterjee (Linkedin) · Ye Tu (LinkedIn Corporation)

Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets
Rui Luo (University College London) · Qiang Zhang (University College London) · Yaodong Yang (University College London) · Jun Wang (JD AI Research & UCL)

Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration
Yao Liu (Stanford University) · Adith Swaminathan (Microsoft Research) · Alekh Agarwal (Microsoft Research) · Emma Brunskill (Stanford University)

Deep Transformers with Latent Depth
Xian Li (Facebook) · Asa Cooper Stickland (University of Edinburgh) · Yuqing Tang (Facebook AI) · Xiang Kong (Carnegie Mellon University)

Adaptive Shrinkage Estimation for Streaming Graphs
Nesreen Ahmed (Intel Labs) · Nick Duffield (Texas A&M University)

Fourier Spectrum Discrepancies in Deep Network Generated Images
Tarik Dzanic (Texas A&M University) · Karan Shah (Georgia Tech) · Freddie Witherden (Texas A&M University)

All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation
jean barbier (EPFL) · Nicolas Macris (EPFL) · Cynthia Rush (Columbia University)

DisARM: Antithetic Gradient Estimator for Discrete Latent Variables
Zhe Dong (Google Research) · Andriy Mnih (DeepMind) · George Tucker (Google Brain)

Minibatch vs Local SGD for Heterogeneous Distributed Learning
Blake Woodworth (TTIC) · Kumar Kshitij Patel (Toyota Technological Institute at Chicago) · Nati Srebro (TTI-Chicago)

Scalable Black-box Optimization by Learnable Search Space Partition
Linnan Wang (Brown University) · Rodrigo Fonseca (Brown University) · Yuandong Tian (Facebook AI Research)

Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition
Ben Adlam (Google) · Jeffrey Pennington (Google Brain)

Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions
Alessandro Epasto (Google) · Mohammad Mahdian (Google Research) · Vahab Mirrokni (Google Research NYC) · Emmanouil Zampetakis (MIT)

Learning efficient task-dependent representations with synaptic plasticity
Colin Bredenberg (New York University) · Eero Simoncelli (HHMI / New York University) · Cristina Savin (NYU)

Primal-Dual Mesh Convolutional Neural Networks
Francesco Milano (ETH Zurich) · Antonio Loquercio (ETH / University of Zurich) · Antoni Rosinol (MIT) · Davide Scaramuzza (University of Zurich & ETH Zurich, Switzerland) · Luca Carlone (Massachusetts Institute of Technology)

Reparameterizing Mirror Descent as Gradient Descent
Ehsan Amid (University of California, Santa Cruz) · Manfred K. Warmuth (Google Brain)

Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
Mohsen Bayati (Stanford University) · Nima Hamidi (Stanford University) · Ramesh Johari (Stanford University) · Khashayar Khosravi (Google Research)

Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Lijing Wang (University of Virginia) · Dipanjan Ghosh (Industrial AI Labs, Hitachi Americas Ltd.) · Maria Gonzalez Diaz (Industrial AI Lab, Hitachi America Ltd.) · Ahmed Farahat (Industrial AI Lab, Hitachi America, Ltd. R&D) · Mahbubul Alam (Industrial AI Lab, Hitachi America, Ltd. R&D) · Chetan Gupta (Industrial AI Lab, Hitachi America R&D, Hitachi Americas Ltd.) · Jiangzhuo Chen (University of Virginia) · Madhav Marathe (Biocomplexity Institute & Initiative, University of Virginia)

Information Theoretic Regret Bounds for Online Nonlinear Control
Sham Kakade (University of Washington & Microsoft Research) · Akshay Krishnamurthy (Microsoft) · Kendall Lowrey (University of Washington) · Motoya Ohnishi (Paul G. Allen School of Computer Science & Engineering) · Wen Sun (Microsoft Research NYC)

f-Divergence Variational Inference
Neng Wan (University of Illinois at Urbana-Champaign) · Dapeng Li (Anker Innovations) · NAIRA HOVAKIMYAN (UIUC)

Learning Physical Constraints with Neural Projections
Shuqi Yang (Dartmouth College) · Xingzhe He (Dartmouth College) · Bo Zhu (Dartmouth College)

Learning sparse codes from compressed representations with biologically plausible local wiring constraints
Kion Fallah (Georgia Institute of Technology) · Adam A Willats (Georgia Institute of Technology and Emory University) · Ninghao Liu (Texas A&M University) · Christopher Rozell (Georgia Institute of Technology)

An Analysis of SVD for Deep Rotation Estimation
Ameesh Makadia (Google Research) · Jake Levinson (University of Washington) · Kefan Chen (Google) · Noah Snavely (Cornell University and Google AI) · Angjoo Kanazawa (UC Berkeley) · Afshin Rostamizadeh (Google Research) · Carlos Esteves (University of Pennsylvania)

Characterizing emergent representations in a space of candidate learning rules for deep networks
Yinan Cao (University of Oxford) · Christopher Summerfield (University of Oxford) · Andrew Saxe (University of Oxford)

Outlier Robust Mean Estimation with Subgaussian Rates via Stability
Ilias Diakonikolas (UW Madison) · Daniel M. Kane (UCSD) · Ankit Pensia (University of Wisconsin-Madison)

Policy Improvement from Multiple Experts
Ching-An Cheng (Microsoft) · Andrey Kolobov (Microsoft Research) · Alekh Agarwal (Microsoft Research)

Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Chongli Qin (DeepMind) · Yan Wu (DeepMind) · Jost Tobias Springenberg (DeepMind) · Andy Brock (DeepMind) · Jeff Donahue (DeepMind) · Timothy Lillicrap (DeepMind & UCL) · Pushmeet Kohli (DeepMind)

Experimental design for MRI by greedy policy search
Tim Bakker (University of Amsterdam) · Herke van Hoof (University of Amsterdam) · Max Welling (University of Amsterdam / Qualcomm AI Research)

CryptoNAS: Private Inference on a ReLU Budget
Zahra Ghodsi (New York University) · Akshaj Kumar Veldanda (New York University) · Brandon Reagen (New York University) · Siddharth Garg (NYU)

Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice
Rylan Schaeffer (Harvard University) · Mikail C Khona (MIT) · Leenoy Meshulam (Massachusetts Institute of Technology MIT) · Brain Laboratory International (International Brain Laboratory) · Ila Fiete (Massachusetts Institute of Technology)

Graph Information Bottleneck
Tailin Wu (Stanford) · Hongyu Ren (Stanford University) · Pan Li (Stanford University - Purdue University) · Jure Leskovec (Stanford University and Pinterest)

Non-Euclidean Universal Approximation
Anastasis Kratsios (ETH Zürich) · Ievgen Bilokopytov (University of Manitoba)

A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints
Omid Sadeghi (University of Washington) · Prasanna Raut (University of Washington) · Maryam Fazel (University of Washington)

Neural Adaptation Properties Allow Identification of Optimized Neural Codes
Luke Rast (Harvard University) · Jan Drugowitsch (Harvard Medical School)

Parallel Stochastic Approximate Proximal Point Methods
Hilal Asi (Stanford University) · Karan Chadha (Stanford University) · Gary Cheng (Stanford University) · John Duchi (Stanford)

Off-Policy Interval Estimation with Lipschitz Value Iteration
Ziyang Tang (UT Austin) · Yihao Feng (UT Austin) · Na Zhang (Tsinghua University) · Jian Peng (University of Illinois at Urbana-Champaign) · Qiang Liu (UT Austin)

Generative Neurosymbolic Machines
Jindong Jiang (Rutgers University) · Sungjin Ahn (Rutgers University)

Provably adaptive reinforcement learning in metric spaces
Tongyi Cao (University of Massachusetts Amherst) · Akshay Krishnamurthy (Microsoft)

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
Vijil Chenthamarakshan (IBM Research) · Payel Das (IBM Research) · Samuel Hoffman (IBM Research) · Hendrik Strobelt (IBM Research) · Inkit Padhi (IBM Research) · Kar Wai Lim (IBM Singapore) · Ben Hoover (IBM) · Matteo Manica (IBM Research Zürich) · Jannis Born (IBM Research) · Teodoro Laino (IBM Research Zurich) · Aleksandra Mojsilovic (IBM Research)

Finite Versus Infinite Neural Networks: an Empirical Study
Jaehoon Lee (Google Brain) · Samuel Schoenholz (Google Brain) · Jeffrey Pennington (Google Brain) · Ben Adlam (Google) · Lechao Xiao (Google Brain) · Roman Novak (Google Brain) · Jascha Sohl-Dickstein (Google Brain)

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Alex Lee (UC Berkeley) · Anusha Nagabandi (UC Berkeley) · Pieter Abbeel (UC Berkeley & covariant.ai) · Sergey Levine (UC Berkeley)

Robust Persistence Diagrams using Reproducing Kernels
Siddharth Vishwanath (The Pennsylvania State University) · Kenji Fukumizu (Institute of Statistical Mathematics / Preferred Networks / RIKEN AIP) · Satoshi Kuriki (Institute of Statistical Mathematics) · Bharath Sriperumbudur (Penn State University)

Learning Global Transparent Models consistent with Local Contrastive Explanations
Tejaswini Pedapati (IBM Research) · Avinash Balakrishnan (IBM) · Karthikeyan Shanmugam (IBM Research, NY) · Amit Dhurandhar (IBM Research)

Understanding Global Feature Contributions Through Additive Importance Measures
Ian Covert (University of Washington) · Scott Lundberg (Microsoft Research) · Su-In Lee (University of Washington)

Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
Virginia Rutten (Gatsby Computational Neuroscience Unit (UCL)) · Alberto Bernacchia (MediaTek Research) · Maneesh Sahani (Gatsby Unit, UCL) · Guillaume Hennequin (Cambridge)

Online Neural Connectivity Estimation with Noisy Group Testing
Anne Draelos (Duke University) · John Pearson (Duke University)

Contrastive learning of global and local features for medical image segmentation with limited annotations
Krishna Chaitanya (ETH Zurich) · Ertunc Erdil (ETH Zurich) · Neerav Karani (ETH Zurich) · Ender Konukoglu (ETH Zurich)

Autoencoders that don't overfit towards the Identity
Harald Steck (Netflix)

An Asymptotically Optimal Primal-Dual Incremental Algorithm for Linear Contextual Bandits
Andrea Tirinzoni (Politecnico di Milano) · Matteo Pirotta (Facebook AI Research) · Marcello Restelli (Politecnico di Milano) · Alessandro Lazaric (Facebook Artificial Intelligence Research)

Adversarial Attacks on Deep Graph Matching
Zijie Zhang (Auburn University) · Zeru Zhang (Auburn University) · Yang Zhou (Auburn University) · Yelong Shen (Microsoft Dynamics 365 AI) · Ruoming Jin (Kent State University) · Dejing Dou (" University of Oregon, USA")

Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
Shafi Goldwasser (The Simons Institute for the Theory of Computing) · Adam Tauman Kalai (Microsoft Research) · Yael Kalai (Micr) · Omar Montasser (Toyota Technological Institute at Chicago)

Conformal Symplectic and Relativistic Optimization
Guilherme Starvaggi Franca (University of California, Berkeley) · Jeremias Sulam (Johns Hopkins University) · Daniel Robinson (Johns Hopkins University) · Rene Vidal (Johns Hopkins University, USA)

General Transportability of Soft Interventions: Completeness Results
Juan Correa (Columbia University) · Elias Bareinboim (Columbia University)

Factor Graph Grammars
David Chiang (University of Notre Dame) · Darcey Riley (University of Notre Dame)

Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations
Joshua Glaser (Columbia) · Matthew Whiteway (Columbia University) · John Cunningham (University of Columbia) · Liam Paninski (Columbia University) · Scott Linderman (Stanford University)

CoSE: Compositional Stroke Embeddings
Emre Aksan (ETH Zurich) · Thomas Deselaers (Apple) · Andrea Tagliasacchi (Google Research, Brain) · Otmar Hilliges (ETH Zurich)

Inverse Reinforcement Learning from a Gradient-based Learner
Giorgia Ramponi (Politecnico di Milano) · Gianluca Drappo (Politecnico di Milano) · Marcello Restelli (Politecnico di Milano)

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
Sumanth Dathathri (DeepMind) · Krishnamurthy Dvijotham (DeepMind) · Alexey Kurakin (Google Brain) · Aditi Raghunathan (Stanford University) · Jonathan Uesato (DeepMind) · Rudy Bunel (Deepmind) · Shreya Shankar (Stanford University) · Jacob Steinhardt (UC Berkeley) · Ian Goodfellow (Google Brain) · Percy Liang (Stanford University) · Pushmeet Kohli (DeepMind)

Adapting to misspecification in linear contextual bandits and beyond
Dylan Foster (MIT) · Claudio Gentile (Google Research) · Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research) · Julian Zimmert (Google)

Efficient estimation of neural tuning during naturalistic behavior
Edoardo Balzani (New York University) · Kaushik Lakshminarasimhan (Columbia University) · Dora Angelaki (New York University) · Cristina Savin (NYU)

Rethinking pooling in graph neural networks
Diego Mesquita (Aalto University) · Amauri Souza (Federal Institute of Ceara) · Samuel Kaski (Aalto University and University of Manchester)

Optimal Topology Design for Cross-Silo Federated Learning
Othmane MARFOQ (Inria / Accenture) · CHUAN XU (Inria Sophia Antipolis) · Giovanni Neglia (Inria) · Richard Vidal (Accenture)

Transferable Graph Optimizers for ML Compilers
Yanqi Zhou (Google Brain) · Sudip Roy (Google) · Amirali Abdolrashidi (UC Riverside) · Daniel Wong (Carnegie Mellon University) · Peter Ma (Google) · Qiumin Xu (Google) · Hanxiao Liu (Google Brain) · Phitchaya Phothilimtha (Google Brain) · Shen Wang (Google Inc) · Anna Goldie (Google Brain / Stanford) · Azalia Mirhoseini (Google Brain) · James Laudon (Google)

Testing Determinantal Point Processes
Khashayar Gatmiry (Massachusetts Institute of Technology) · Maryam Aliakbarpour (MIT) · Stefanie Jegelka (MIT)

Gradient Boosted Normalizing Flows
Robert Giaquinto (University of Minnesota) · Arindam Banerjee (University of Minnesota, Twin Cities)

Belief Propagation Neural Networks
Jonathan Kuck (Stanford) · Shuvam Chakraborty (Stanford University) · Hao Tang (Shanghai Jiao Tong University) · Rachel Luo (Stanford University) · Jiaming Song (Stanford University) · Ashish Sabharwal (Allen Institute for AI) · Stefano Ermon (Stanford)

Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks
Jingqiu Ding (ETH Zurich) · Samuel Hopkins (UC Berkeley) · David Steurer (ETH Zurich)

Language-Conditioned Imitation Learning for Robot Manipulation Tasks
Simon Stepputtis (Arizona State University) · Joseph Campbell (Arizona State University) · Mariano Phielipp (Intel AI Labs) · Stefan Lee (Oregon State University) · Chitta Baral (Arizona State University) · Heni Ben Amor (Arizona State University)

Incorporating Interpretable Output Constraints in Bayesian Neural Networks
Wanqian Yang (Harvard University) · Lars Lorch (Harvard) · Moritz Graule (Harvard University) · Himabindu Lakkaraju (Harvard) · Finale Doshi-Velez (Harvard)

Network Diffusions via Neural Mean-Field Dynamics
Shushan He (Georgia State University) · Hongyuan Zha (Georgia Tech) · Xiaojing Ye (Georgia State University)

Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class
Mingyuan Zhang (University of Pennsylvania) · Shivani Agarwal (University of Pennsylvania)

Coresets for Near-Convex Functions
Morad Tukan (University of Haifa) · Alaa Maalouf (The University of Haifa) · Dan Feldman (University of Haifa)

Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration
Andrea Zanette (Stanford University) · Alessandro Lazaric (Facebook Artificial Intelligence Research) · Mykel J Kochenderfer (Stanford University) · Emma Brunskill (Stanford University)

Pushing the Limits of Narrow Precision Inferencing at Cloud-Scale with Bounding-Box Floating-Point
Bita Darvish Rouhani (Microsoft) · Daniel Lo (Microsoft) · Ritchie Zhao (Microsoft) · Ming Liu (Microsoft) · Jeremy Fowers (Microsoft) · Kalin Ovtcharov (Microsoft) · Anna Vinogradsky (Caltech) · Sarah Massengill (Microsoft) · Lita Yang (Microsoft) · Ray Bittner (Microsoft Research) · Alessandro Forin (Microsoft) · Haishan Zhu (Microsoft) · Taesik Na (Microsoft) · Prerak Patel (Microsoft) · Shuai Che (Microsoft) · Lok Chand Koppaka (Microsoft) · Steve Reinhardt (Microsoft) · Sitaram Lanka (Microsoft) · XIA SONG (Microsoft) · Subhojit Som (Microsoft) · Kaustav Das (Microsoft) · Saurabh K T (Microsoft Corporation) · Eric Chung (Microsoft) · Doug Burger (Microsoft Research)

Self-Learning Transformations for Improving Gaze and Head Redirection
Yufeng Zheng (ETH Zurich) · Seonwook Park (ETH Zurich) · Xucong Zhang (ETH Zurich) · Shalini De Mello (NVIDIA) · Otmar Hilliges (ETH Zurich)

On the Error Resistance of Hinge-Loss Minimization
Kunal Talwar (Apple)

Efficient Planning in Large MDPs with Weak Linear Function Approximation
Roshan Shariff (University of Alberta) · Csaba Szepesvari (DeepMind / University of Alberta)

Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences
Bowen Baker (OpenAI)

Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction
Mariya Toneva (Carnegie Mellon University) · Otilia Stretcu (Carnegie Mellon University) · Barnabas Poczos (Carnegie Mellon University) · Leila Wehbe (Carnegie Mellon University) · Tom Mitchell (Carnegie Mellon University)

PRANK: motion Prediction based on RANKing
Yuriy Biktairov (Yandex) · Maxim Stebelev (Yandex) · Boris Yangel (Yandex) · Irina Rudenko (Yandex) · Oleh Shliazhko (Yandex)

The Potts-Ising model for discrete multivariate data
Zahra Razaee (Cedars Sinai) · Arash Amini (UCLA)

Gibbs Sampling with People
Peter Harrison (Max Planck Institute for Empirical Aesthetics) · Raja Marjieh (Max Planck Institute for Empirical Aesthetics) · Federico G Adolfi (Max-Planck Institute AE, Frankfurt, Germany) · Pol van Rijn (Max Planck Institute for Empirical Aesthetics) · Manuel Anglada-Tort (Max Planck Institute for Empirical Aesthetics) · Ofer Tchernichovski (Hunter College, CUNY) · Pauline Larrouy-Maestri (Max-Planck-Institute of Empircal Aesthetics) · Nori Jacoby (Max Planck Institute for Empirical Aesthetics)

Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds
Hassan Hafez-Kolahi (Sharif University of Technology) · Zeinab Golgooni (Sharif University of Technology) · Shohreh Kasaei (Sharif University of Technology) · Mahdieh Soleymani (Sharif University of Technology)

Learning Causal Effects via Weighted Empirical Risk Minimization
Yonghan Jung (Purdue University) · Jin Tian (Iowa State University) · Elias Bareinboim (Columbia University)

Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing
Arun Jambulapati (Stanford University) · Jerry Li (Microsoft) · Kevin Tian (Stanford University)

On Power Laws in Deep Ensembles
Ekaterina Lobacheva (Samsung-HSE Laboratory) · Nadezhda Chirkova (Higher School of Economics, Samsung-HSE Laboratory) · Maxim Kodryan (Samsung-HSE Laboratory, National Research University Higher School of Economics) · Dmitry Vetrov (Higher School of Economics, Samsung AI Center, Moscow)

High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization
Qing Feng (Facebook) · Ben Letham (Facebook) · Hongzi Mao (MIT) · Eytan Bakshy (Facebook)

Certified Defense to Image Transformations via Randomized Smoothing
Marc Fischer (ETH Zurich) · Maximilian Baader (ETH Zürich) · Martin Vechev (ETH Zurich, Switzerland)

A meta-learning approach to (re)discover plasticity rules that carve a desired function to a neural network
Basile Confavreux (University of Oxford) · Friedemann Zenke (Friedrich Miescher Institute) · Everton Agnes (University of Oxford) · Timothy Lillicrap (DeepMind & UCL) · Tim Vogels (Institute of Science and Technology)

Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning
Weili Nie (Rice University) · Zhiding Yu (NVIDIA) · Lei Mao (NVIDIA) · Ankit Patel (Rice University) · Yuke Zhu (University of Texas - Austin) · Anima Anandkumar (NVIDIA / Caltech)

On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission
Mrinank Sharma (University of Oxford) · Sören Mindermann (University of Oxford) · Jan Brauner (University of Oxford) · Gavin Leech (University of Bristol) · Anna Stephenson (Harvard University) · Tomáš Gavenčiak (Independent researcher) · Jan Kulveit (University of Oxford) · Yee Whye Teh (University of Oxford, DeepMind) · Leonid Chindelevitch (Simon Fraser University) · Yarin Gal (University of Oxford)

Learning abstract structure for drawing by efficient motor program induction
Lucas Tian (MIT) · Kevin Ellis (MIT) · Marta Kryven (Massachusetts Institute of Technology) · Josh Tenenbaum (MIT)

Learning Certified Individually Fair Representations
Anian Ruoss (ETH Zurich) · Mislav Balunovic (ETH Zurich) · Marc Fischer (ETH Zurich) · Martin Vechev (ETH Zurich, Switzerland)

Interpretable Sequence Learning for Covid-19 Forecasting
Sercan Arik (Google) · Chun-Liang Li (Google) · Martin Nikoltchev (Google) · Rajarishi Sinha (Google) · Arkady Epshteyn (Google) · Jinsung Yoon (Google) · Long Le (Google) · Vikas Menon (Google) · Shashank Singh (Google) · Yash Sonthalia (Google) · Hootan Nakhost (Google) · Leyou Zhang (Google) · Elli Kanal (Google) · Tomas Pfister (Google)

Improving Inference for Neural Image Compression
Yibo Yang (University of California, Irivine) · Robert Bamler (University of California at Irvine) · Stephan Mandt (University of California, Irivine)

Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning
Julius Berner (University of Vienna) · Markus Dablander (University of Oxford) · Philipp Grohs (University of Vienna)

Pre-training via Paraphrasing
Mike Lewis (Facebook AI Research) · Marjan Ghazvininejad (Facebook AI Research) · Gargi Ghosh (Facebook) · Armen Aghajanyan (Facebook) · Sida Wang (Facebook AI Research) · Luke Zettlemoyer (University of Washington and Allen Institute for Artificial Intelligence)

Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention
Ekta Sood (University of Stuttgart) · Simon Tannert (University of Stuttgart) · Philipp Mueller (VIS, University of Stuttgart) · Andreas Bulling (University of Stuttgart)

Explicit Regularization is Stronger than Implicit Bias: A Study of SGD around Bad Global Minima
Shengchao Liu (MILA, Université de Montréal) · Dimitris Papailiopoulos (University of Wisconsin-Madison) · Dimitris Achlioptas (University of Athens)

Deep Smoothing of the Implied Volatility Surface
Damien Ackerer (Swissquote) · Natasa Tagasovska (EPFL) · Thibault Vatter (Columbia University)

Distribution-free binary classification: prediction sets, confidence intervals and calibration
Chirag Gupta (Carnegie Mellon University) · Aleksandr Podkopaev (Carnegie Mellon University) · Aaditya Ramdas (CMU)

Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing
Vishaal Krishnan (University of California, Riverside) · Abed AlRahman Al Makdah (University of California, Riverside) · Fabio Pasqualetti (University of California, Riverside)

Agnostic Learning with Multiple Objectives
Corinna Cortes (Google Research) · Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research) · Javier Gonzalvo (Google) · Dmitry Storcheus (Google Research)

Model Class Reliance for Random Forests
Gavin Smith (University of Nottingham) · Roberto Mansilla (University of Nottingham) · James Goulding (University of Nottingham)

Mitigating Local Identifiability in Probabilistic BoxEmbeddings
Shib Dasgupta (University of Massachusetts Amherst) · Michael Boratko (UMass Amherst) · Dongxu Zhang (University of Massachusetts Amherst) · Luke Vilnis (University of Massachusetts, Amherst) · Xiang Li (UMass Amherst) · Andrew McCallum (UMass Amherst)