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Timezone: America/Los_Angeles
Full Schedule Mon 12/6 Tue 12/7 Wed 12/8 Thu 12/9 Fri 12/10 Sat 12/11 Sun 12/12 Mon 12/13 Tue 12/14
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Poster
12:30 AM - 2:00 AM
266 Events in this session
Adversarial Reweighting for Partial Domain Adaptation
Xiang Gu · Xi Yu · Yan Yang · Jian Sun · Zongben Xu
Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels
Hao Wang · Yizhe Huang · Rui Gao · Flavio Calmon
Anti-Backdoor Learning: Training Clean Models on Poisoned Data
Yige Li · Xixiang Lyu · Nodens Koren · Lingjuan Lyu · Bo Li · Xingjun Ma
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning
Nan Ding · Xi Chen · Tomer Levinboim · Sebastian Goodman · Radu Soricut
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression
Zhize Li · Peter Richtarik
Co-evolution Transformer for Protein Contact Prediction
He Zhang · Fusong Ju · Jianwei Zhu · Liang He · Bin Shao · Nanning Zheng · Tie-Yan Liu
CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction
Leo Lebrat · Rodrigo Santa Cruz · Frederic de Gournay · Darren Fu · Pierrick Bourgeat · Jurgen Fripp · Clinton Fookes · Olivier Salvado
Counterexample Guided RL Policy Refinement Using Bayesian Optimization
Briti Gangopadhyay · Pallab Dasgupta
Deep Contextual Video Compression
Jiahao Li · Bin Li · Yan Lu
Diversity Matters When Learning From Ensembles
Giung Nam · Jongmin Yoon · Yoonho Lee · Juho Lee
Faster Non-asymptotic Convergence for Double Q-learning
Lin Zhao · Huaqing Xiong · Yingbin Liang
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
Kazu Ghalamkari · Mahito Sugiyama
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
Hengrui Zhang · Qitian Wu · Junchi Yan · David Wipf · Philip S Yu
Gradient Inversion with Generative Image Prior
Jinwoo Jeon · jaechang Kim · Kangwook Lee · Sewoong Oh · Jungseul Ok
Graphical Models in Heavy-Tailed Markets
José Vinícius de Miranda Cardoso · Jiaxi Ying · Daniel Palomar
Hessian Eigenspectra of More Realistic Nonlinear Models
Zhenyu Liao · Michael Mahoney
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis
Patrick Esser · Robin Rombach · Andreas Blattmann · Bjorn Ommer
Integrating Tree Path in Transformer for Code Representation
Han Peng · Ge Li · Wenhan Wang · YunFei Zhao · Zhi Jin
Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning
Aakash Kaku · Sahana Upadhya · Narges Razavian
Learning interaction rules from multi-animal trajectories via augmented behavioral models
Keisuke Fujii · Naoya Takeishi · Kazushi Tsutsui · Emyo Fujioka · Nozomi Nishiumi · Ryoya Tanaka · Mika Fukushiro · Kaoru Ide · Hiroyoshi Kohno · Ken Yoda · Susumu Takahashi · Shizuko Hiryu · Yoshinobu Kawahara
Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach
Fan Yang · Kai He · Linxiao Yang · Hongxia Du · Jingbang Yang · Bo Yang · Liang Sun
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
Aditya Kusupati · Matthew Wallingford · Vivek Ramanujan · Raghav Somani · Jae Sung Park · Krishna Pillutla · Prateek Jain · Sham Kakade · Ali Farhadi
Manifold Topology Divergence: a Framework for Comparing Data Manifolds.
Serguei Barannikov · Ilya Trofimov · Grigorii Sotnikov · Ekaterina Trimbach · Alexander Korotin · Alexander Filippov · Evgeny Burnaev
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables
Jakob Runge
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform
Achille Thin · Yazid Janati El Idrissi · Sylvain Le Corff · Charles Ollion · Eric Moulines · Arnaud Doucet · Alain Durmus · Christian X Robert
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
Jiun Tian Hoe · Kam Woh Ng · Tianyu Zhang · Chee Seng Chan · Yi-Zhe Song · Tao Xiang
Overlapping Spaces for Compact Graph Representations
Kirill Shevkunov · Liudmila Prokhorenkova
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning
Tao Yu · Cuiling Lan · Wenjun Zeng · Mingxiao Feng · Zhizheng Zhang · Zhibo Chen
Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning
Milad Abdollahzadeh · Touba Malekzadeh · Ngai-Man (Man) Cheung
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression
Ziyu Wang · Yuhao Zhou · Tongzheng Ren · Jun Zhu
See More for Scene: Pairwise Consistency Learning for Scene Classification
Gongwei Chen · Xinhang Song · Bohan Wang · Shuqiang Jiang
Tactical Optimism and Pessimism for Deep Reinforcement Learning
Ted Moskovitz · Jack Parker-Holder · Aldo Pacchiano · Michael Arbel · Michael Jordan
Transformers Generalize DeepSets and Can be Extended to Graphs & Hypergraphs
Jinwoo Kim · Saeyoon Oh · Seunghoon Hong
Uncertainty-Driven Loss for Single Image Super-Resolution
Qian Ning · Weisheng Dong · Xin Li · Jinjian Wu · GUANGMING Shi
Universal Off-Policy Evaluation
Yash Chandak · Scott Niekum · Bruno da Silva · Erik Learned-Miller · Emma Brunskill · Philip Thomas
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
Jiayu Chen · Yuanxin Zhang · Yuanfan Xu · Huimin Ma · Huazhong Yang · Jiaming Song · Yu Wang · Yi Wu
What can linearized neural networks actually say about generalization?
Guillermo Ortiz-Jimenez · Seyed-Mohsen Moosavi-Dezfooli · Pascal Frossard
Width-based Lookaheads with Learnt Base Policies and Heuristics Over the Atari-2600 Benchmark
Stefan O'Toole · Nir Lipovetzky · Miquel Ramirez · Adrian Pearce
A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval
Xiu-Shen Wei · Yang Shen · Xuhao Sun · Han-Jia Ye · Jian Yang
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
Kai Xu · Akash Srivastava · Dan Gutfreund · Felix Sosa · Tomer Ullman · Josh Tenenbaum · Charles Sutton
A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering
Tom Hess · Michal Moshkovitz · Sivan Sabato
Activation Sharing with Asymmetric Paths Solves Weight Transport Problem without Bidirectional Connection
Sunghyeon Woo · Jeongwoo Park · Jiwoo Hong · Dongsuk Jeon
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations
Vihari Piratla · Soumen Chakrabarti · Sunita Sarawagi
Actively Identifying Causal Effects with Latent Variables Given Only Response Variable Observable
Tian-Zuo Wang · Zhi-Hua Zhou
Adaptive Data Augmentation on Temporal Graphs
Yiwei Wang · Yujun Cai · Yuxuan Liang · Henghui Ding · Changhu Wang · Siddharth Bhatia · Bryan Hooi
Adder Attention for Vision Transformer
Han Shu · Jiahao Wang · Hanting Chen · Lin Li · Yujiu Yang · Yunhe Wang
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
Sen Cui · Weishen Pan · Jian Liang · Changshui Zhang · Fei Wang
Agent Modelling under Partial Observability for Deep Reinforcement Learning
Georgios Papoudakis · Filippos Christianos · Stefano Albrecht
A Gradient Method for Multilevel Optimization
Ryo Sato · Mirai Tanaka · Akiko Takeda
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias
Lu Yu · Krishnakumar Balasubramanian · Stanislav Volgushev · Murat Erdogdu
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning
Blake Woodworth · Nathan Srebro
A Note on Sparse Generalized Eigenvalue Problem
Yunfeng Cai · Guanhua Fang · Ping Li
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data
Jonathan Schmidt · Nicholas Krämer · Philipp Hennig
A Provably Efficient Sample Collection Strategy for Reinforcement Learning
Jean Tarbouriech · Matteo Pirotta · Michal Valko · Alessandro Lazaric
Artistic Style Transfer with Internal-external Learning and Contrastive Learning
Haibo Chen · lei zhao · Zhizhong Wang · Huiming Zhang · Zhiwen Zuo · Ailin Li · Wei Xing · Dongming Lu
Associating Objects with Transformers for Video Object Segmentation
Zongxin Yang · Yunchao Wei · Yi Yang
Asynchronous Decentralized Online Learning
Jiyan Jiang · Wenpeng Zhang · Jinjie GU · Wenwu Zhu
Asynchronous Decentralized SGD with Quantized and Local Updates
Giorgi Nadiradze · Amirmojtaba Sabour · Peter Davies · Shigang Li · Dan Alistarh
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation
Fenglin Liu · Chenyu You · Xian Wu · Shen Ge · Sheng wang · Xu Sun
Automatic Data Augmentation for Generalization in Reinforcement Learning
Roberta Raileanu · Maxwell Goldstein · Denis Yarats · Ilya Kostrikov · Rob Fergus
Bandits with many optimal arms
Rianne de Heide · James Cheshire · Pierre Ménard · Alexandra Carpentier
Batched Thompson Sampling
Cem Kalkanli · Ayfer Ozgur
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
Mingguo He · Zhewei Wei · zengfeng Huang · Hongteng Xu
Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning
Ekdeep S Lubana · Robert Dick · Hidenori Tanaka
Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation
Robert Vandermeulen · Antoine Ledent
Beyond the Signs: Nonparametric Tensor Completion via Sign Series
Chanwoo Lee · Miaoyan Wang
Boost Neural Networks by Checkpoints
Feng Wang · Guoyizhe Wei · Qiao Liu · Jinxiang Ou · xian wei · Hairong Lv
BooVI: Provably Efficient Bootstrapped Value Iteration
Boyi Liu · Qi Cai · Zhuoran Yang · Zhaoran Wang
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs
Han Zhong · Jiayi Huang · Lin Yang · Liwei Wang
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
Hyunsoo Chung · Jungtaek Kim · Boris Knyazev · Jinhwi Lee · Graham Taylor · Jaesik Park · Minsu Cho
Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication
Łukasz Kuciński · Tomasz Korbak · Paweł Kołodziej · Piotr Miłoś
CATs: Cost Aggregation Transformers for Visual Correspondence
Seokju Cho · Sunghwan Hong · Sangryul Jeon · Yunsung Lee · Kwanghoon Sohn · Seungryong Kim
Causal Bandits with Unknown Graph Structure
Yangyi Lu · Amirhossein Meisami · Ambuj Tewari
Causal Identification with Matrix Equations
Sanghack Lee · Elias Bareinboim
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote
Yi-Shan Wu · Andres Masegosa · Stephan Lorenzen · Christian Igel · Yevgeny Seldin
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation
Ankit Singh
Combinatorial Pure Exploration with Bottleneck Reward Function
Yihan Du · Yuko Kuroki · Wei Chen
Compositional Reinforcement Learning from Logical Specifications
Kishor Jothimurugan · Suguman Bansal · Osbert Bastani · Rajeev Alur
Conformal Prediction using Conditional Histograms
Matteo Sesia · Yaniv Romano
Conformal Time-series Forecasting
Kamile Stankeviciute · Ahmed M. Alaa · Mihaela van der Schaar
Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes
Sara Sangalli · Ertunc Erdil · Andeas Hötker · Olivio Donati · Ender Konukoglu
Continuous Mean-Covariance Bandits
Yihan Du · Siwei Wang · Zhixuan Fang · Longbo Huang
Coresets for Time Series Clustering
Lingxiao Huang · K Sudhir · Nisheeth Vishnoi
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning
Xiong-Hui Chen · Shengyi Jiang · Feng Xu · Zongzhang Zhang · Yang Yu
Curriculum Disentangled Recommendation with Noisy Multi-feedback
Hong Chen · Yudong Chen · Xin Wang · Ruobing Xie · Rui Wang · Feng Xia · Wenwu Zhu
Cycle Self-Training for Domain Adaptation
Hong Liu · Jianmin Wang · Mingsheng Long
Decentralized Q-learning in Zero-sum Markov Games
Muhammed Sayin · Kaiqing Zhang · David Leslie · Tamer Basar · Asuman Ozdaglar
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks
Rong Zhu · Mattia Rigotti
Deep Conditional Gaussian Mixture Model for Constrained Clustering
Laura Manduchi · Kieran Chin-Cheong · Holger Michel · Sven Wellmann · Julia Vogt
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
Ritsugen Jo · Heishiro Kanagawa · Arthur Gretton
Denoising Normalizing Flow
Christian Horvat · Jean-Pascal Pfister
De-randomizing MCMC dynamics with the diffusion Stein operator
Zheyang Shen · Markus Heinonen · Samuel Kaski
Differentially Private Multi-Armed Bandits in the Shuffle Model
Jay Tenenbaum · Haim Kaplan · Yishay Mansour · Uri Stemmer
Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation
Ying Sun · Hengshu Zhu · Chuan Qin · Fuzhen Zhuang · Qing He · Hui Xiong
Disentangled Contrastive Learning on Graphs
Haoyang Li · Xin Wang · Ziwei Zhang · Zehuan Yuan · Hang Li · Wenwu Zhu
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions
Pushi Zhang · Xiaoyu Chen · Li Zhao · Wei Xiong · Tao Qin · Tie-Yan Liu
Do Different Tracking Tasks Require Different Appearance Models?
Zhongdao Wang · Hengshuang Zhao · Ya-Li Li · Shengjin Wang · Philip Torr · Luca Bertinetto
Doubly Robust Thompson Sampling with Linear Payoffs
Wonyoung Kim · Gi-Soo Kim · Myunghee Cho Paik
DRONE: Data-aware Low-rank Compression for Large NLP Models
Patrick Chen · Hsiang-Fu Yu · Inderjit Dhillon · Cho-Jui Hsieh
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
Pál András Papp · Karolis Martinkus · Lukas Faber · Roger Wattenhofer
Dual Progressive Prototype Network for Generalized Zero-Shot Learning
Chaoqun Wang · Shaobo Min · Xuejin Chen · Xiaoyan Sun · Houqiang Li
Dynamic Resolution Network
Mingjian Zhu · Kai Han · Enhua Wu · Qiulin Zhang · Ying Nie · Zhenzhong Lan · Yunhe Wang
EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback
Peter Richtarik · Igor Sokolov · Ilyas Fatkhullin
Effective Meta-Regularization by Kernelized Proximal Regularization
Weisen Jiang · James Kwok · Yu Zhang
Efficient and Local Parallel Random Walks
Michael Kapralov · Silvio Lattanzi · Navid Nouri · Jakab Tardos
Efficient Combination of Rematerialization and Offloading for Training DNNs
Olivier Beaumont · Lionel Eyraud-Dubois · Alena Shilova
Efficient Learning of Discrete-Continuous Computation Graphs
David Friede · Mathias Niepert
E(n) Equivariant Normalizing Flows
Victor Garcia Satorras · Emiel Hoogeboom · Fabian Fuchs · Ingmar Posner · Max Welling
Entropy-based adaptive Hamiltonian Monte Carlo
Marcel Hirt · Michalis Titsias · Petros Dellaportas
Error Compensated Distributed SGD Can Be Accelerated
Xun Qian · Peter Richtarik · Tong Zhang
Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error
Jonah Brown-Cohen
Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights
Dong Quan Vu · Kimon Antonakopoulos · Panayotis Mertikopoulos
Few-Round Learning for Federated Learning
Younghyun Park · Dong-Jun Han · Do-Yeon Kim · Jun Seo · Jaekyun Moon
Generalizable Multi-linear Attention Network
Tao Jin · Zhou Zhao
Generalization Guarantee of SGD for Pairwise Learning
Yunwen Lei · Mingrui Liu · Yiming Ying
Global-aware Beam Search for Neural Abstractive Summarization
Ye Ma · Zixun Lan · Lu Zong · Kaizhu Huang
Global Convergence of Online Optimization for Nonlinear Model Predictive Control
Sen Na
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
Baihe Huang · Kaixuan Huang · Sham Kakade · Jason Lee · Qi Lei · Runzhe Wang · Jiaqi Yang
Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning
Xinyi Xu · Lingjuan Lyu · Xingjun Ma · Chenglin Miao · Chuan Sheng Foo · Bryan Kian Hsiang Low
Graph Differentiable Architecture Search with Structure Learning
Yijian Qin · Xin Wang · Zeyang Zhang · Wenwu Zhu
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
Maximilian Stadler · Bertrand Charpentier · Simon Geisler · Daniel Zügner · Stephan Günnemann
GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction
Longyuan Li · Jian Yao · Li Wenliang · Tong He · Tianjun Xiao · Junchi Yan · David Wipf · Zheng Zhang
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
Andrew Szot · Alexander Clegg · Eric Undersander · Erik Wijmans · Yili Zhao · John Turner · Noah Maestre · Mustafa Mukadam · Devendra Singh Chaplot · Oleksandr Maksymets · Aaron Gokaslan · Vladimír Vondruš · Sameer Dharur · Franziska Meier · Wojciech Galuba · Angel Chang · Zsolt Kira · Vladlen Koltun · Jitendra Malik · Manolis Savva · Dhruv Batra
Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning
Hayeon Lee · Sewoong Lee · Song Chong · Sung Ju Hwang
History Aware Multimodal Transformer for Vision-and-Language Navigation
Shizhe Chen · Pierre-Louis Guhur · Cordelia Schmid · Ivan Laptev
How Tight Can PAC-Bayes be in the Small Data Regime?
Andrew Foong · Wessel Bruinsma · David Burt · Richard Turner
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning
Shiming Chen · Guosen Xie · Yang Liu · Qinmu Peng · Baigui Sun · Hao Li · Xinge You · Ling Shao
Hypergraph Propagation and Community Selection for Objects Retrieval
Guoyuan An · Yuchi Huo · Sung-eui Yoon
IA-RED$^2$: Interpretability-Aware Redundancy Reduction for Vision Transformers
Bowen Pan · Rameswar Panda · Yifan Jiang · Zhangyang Wang · Rogerio Feris · Aude Oliva
Identifiability in inverse reinforcement learning
Haoyang Cao · Samuel Cohen · Lukasz Szpruch
Identification of the Generalized Condorcet Winner in Multi-dueling Bandits
Björn Haddenhorst · Viktor Bengs · Eyke Hüllermeier
iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder
Shifeng Zhang · Ning Kang · Tom Ryder · Zhenguo Li
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
Zihan Zhang · Jiaqi Yang · Xiangyang Ji · Simon Du
Independent mechanism analysis, a new concept?
Luigi Gresele · Julius von Kügelgen · Vincent Stimper · Bernhard Schölkopf · Michel Besserve
INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding
Shuwen Liu · Bernardo Grau · Ian Horrocks · Egor Kostylev
Information Directed Reward Learning for Reinforcement Learning
David Lindner · Matteo Turchetta · Sebastian Tschiatschek · Kamil Ciosek · Andreas Krause
Instance-optimal Mean Estimation Under Differential Privacy
Ziyue Huang · Yuting Liang · Ke Yi
Integrated Latent Heterogeneity and Invariance Learning in Kernel Space
Jiashuo Liu · Zheyuan Hu · Peng Cui · Bo Li · Zheyan Shen
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models
Matej Zečević · Devendra Dhami · Athresh Karanam · Sriraam Natarajan · Kristian Kersting
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks
Tolga Birdal · Aaron Lou · Leonidas Guibas · Umut Simsekli
IRM—when it works and when it doesn't: A test case of natural language inference
Yana Dranker · He He · Yonatan Belinkov
Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias
Raanan Rohekar · Shami Nisimov · Yaniv Gurwicz · Gal Novik
It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems
Regev Cohen · Yochai Blau · Daniel Freedman · Ehud Rivlin
Kernel Functional Optimisation
Arun Kumar Anjanapura Venkatesh · Alistair Shilton · Santu Rana · Sunil Gupta · Svetha Venkatesh
Knowledge-Adaptation Priors
Mohammad Emtiyaz Khan · Siddharth Swaroop
KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network
YU HAO · Xin Cao · Yufan Sheng · Yixiang Fang · Wei Wang
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning
Junsu Kim · Younggyo Seo · Jinwoo Shin
Latent Matters: Learning Deep State-Space Models
Alexej Klushyn · Richard Kurle · Maximilian Soelch · Botond Cseke · Patrick van der Smagt
Learning a Single Neuron with Bias Using Gradient Descent
Gal Vardi · Gilad Yehudai · Ohad Shamir
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations
Yuping Luo · Tengyu Ma
Learning curves of generic features maps for realistic datasets with a teacher-student model
Bruno Loureiro · Cedric Gerbelot · Hugo Cui · Sebastian Goldt · Florent Krzakala · Marc Mezard · Lenka Zdeborová
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent
Priyank Jaini · Lars Holdijk · Max Welling
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems
Andreas Schlaginhaufen · Philippe Wenk · Andreas Krause · Florian Dorfler
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks
Pascal Esser · Leena Chennuru Vankadara · Debarghya Ghoshdastidar
Learning to Combine Per-Example Solutions for Neural Program Synthesis
Disha Shrivastava · Hugo Larochelle · Daniel Tarlow
Learning to Learn Graph Topologies
Xingyue Pu · Tianyue Cao · Xiaoyun Zhang · Xiaowen Dong · Siheng Chen
Lip to Speech Synthesis with Visual Context Attentional GAN
Minsu Kim · Joanna Hong · Yong Man Ro
Low-Fidelity Video Encoder Optimization for Temporal Action Localization
Mengmeng Xu · Juan Manuel Perez Rua · Xiatian Zhu · Bernard Ghanem · Brais Martinez
Low-Rank Subspaces in GANs
Jiapeng Zhu · Ruili Feng · Yujun Shen · Deli Zhao · Zheng-Jun Zha · Jingren Zhou · Qifeng Chen
Making the most of your day: online learning for optimal allocation of time
Etienne Boursier · Tristan Garrec · Vianney Perchet · Marco Scarsini
Meta Internal Learning
Raphael Bensadoun · Shir Gur · Tomer Galanti · Lior Wolf
Meta-Learning for Relative Density-Ratio Estimation
Atsutoshi Kumagai · Tomoharu Iwata · Yasuhiro Fujiwara
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Trent Kyono · Yao Zhang · Alexis Bellot · Mihaela van der Schaar
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
Zhibo Zhu · Ziqi Liu · Ge Jin · Zhiqiang Zhang · Lei Chen · Jun Zhou · Jianyong Zhou
Model-Based Reinforcement Learning via Imagination with Derived Memory
Yao Mu · Yuzheng Zhuang · Bin Wang · Guangxiang Zhu · Wulong Liu · Jianyu Chen · Ping Luo · Shengbo Li · Chongjie Zhang · Jianye Hao
Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model
Antoine Bodin · Nicolas Macris
Model Selection for Bayesian Autoencoders
Ba-Hien Tran · Simone Rossi · Dimitrios Milios · Pietro Michiardi · Edwin Bonilla · Maurizio Filippone
Modular Gaussian Processes for Transfer Learning
Pablo Moreno-Muñoz · Antonio Artes · Mauricio Álvarez
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data
Gongfan Fang · Yifan Bao · Jie Song · Xinchao Wang · Donglin Xie · Chengchao Shen · Mingli Song
Multi-Label Learning with Pairwise Relevance Ordering
Ming-Kun Xie · Sheng-Jun Huang
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs
Jiafan He · Dongruo Zhou · Quanquan Gu
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning
Scott Sussex · Caroline Uhler · Andreas Krause
Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization
Rohan Ghosh · Mehul Motani
Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation
Yufei Wang · Can Xu · Huang Hu · Chongyang Tao · Stephen Wan · Mark Dras · Mark Johnson · Daxin Jiang
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
Mi Luo · Fei Chen · Dapeng Hu · Yifan Zhang · Jian Liang · Jiashi Feng
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis
Jikai Jin · Bohang Zhang · Haiyang Wang · Liwei Wang
Not All Low-Pass Filters are Robust in Graph Convolutional Networks
Heng Chang · Yu Rong · Tingyang Xu · Yatao Bian · Shiji Zhou · Xin Wang · Junzhou Huang · Wenwu Zhu
Numerical Composition of Differential Privacy
Sivakanth Gopi · Yin Tat Lee · Lukas Wutschitz
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration
Runzhe Wu · Yufeng Zhang · Zhuoran Yang · Zhaoran Wang
On learning sparse vectors from mixture of responses
Nikita Polyanskii
Online Meta-Learning via Learning with Layer-Distributed Memory
Sudarshan Babu · Pedro Savarese · Michael Maire
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
Shuyu Cheng · Guoqiang Wu · Jun Zhu
On the Estimation Bias in Double Q-Learning
Zhizhou Ren · Guangxiang Zhu · Hao Hu · Beining Han · Jianglun Chen · Chongjie Zhang
On the Provable Generalization of Recurrent Neural Networks
Lifu Wang · Bo Shen · Bo Hu · Xing Cao
Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD
Bohan Wang · Huishuai Zhang · Jieyu Zhang · Qi Meng · Wei Chen · Tie-Yan Liu
Oracle Complexity in Nonsmooth Nonconvex Optimization
Guy Kornowski · Ohad Shamir
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima
Guangyuan SHI · JIAXIN CHEN · Wenlong Zhang · Li-Ming Zhan · Xiao-Ming Wu
Parameter-free HE-friendly Logistic Regression
Junyoung Byun · Woojin Lee · Jaewook Lee
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization
Benyuan Sun · Hongxing Huo · YI YANG · Bo Bai
Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks
Calvin Tsay · Jan Kronqvist · Alexander Thebelt · Ruth Misener
Personalized Federated Learning With Gaussian Processes
Idan Achituve · Aviv Shamsian · Aviv Navon · Gal Chechik · Ethan Fetaya
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples
Duong Le · Khoi Duc Nguyen · Khoi Nguyen · Quoc-Huy Tran · Rang Nguyen · Binh-Son Hua
PortaSpeech: Portable and High-Quality Generative Text-to-Speech
Yi Ren · Jinglin Liu · Zhou Zhao
Post-Training Sparsity-Aware Quantization
Gil Shomron · Freddy Gabbay · Samer Kurzum · Uri Weiser
Powerpropagation: A sparsity inducing weight reparameterisation
Jonathan Richard Schwarz · Siddhant Jayakumar · Razvan Pascanu · Peter E Latham · Yee Teh
Precise characterization of the prior predictive distribution of deep ReLU networks
Lorenzo Noci · Gregor Bachmann · Kevin Roth · Sebastian Nowozin · Thomas Hofmann
Preserved central model for faster bidirectional compression in distributed settings
Constantin Philippenko · Aymeric Dieuleveut
Proportional Participatory Budgeting with Additive Utilities
Grzegorz Pierczyński · Piotr Skowron · Dominik Peters
PSD Representations for Effective Probability Models
Alessandro Rudi · Carlo Ciliberto
Rectifying the Shortcut Learning of Background for Few-Shot Learning
Xu Luo · Longhui Wei · Liangjian Wen · Jinrong Yang · Lingxi Xie · Zenglin Xu · Qi Tian
Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation
Jingyu Zhao · Yanwen Fang · Guodong Li
Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems
Subhabrata Dutta · Tanya Gautam · Soumen Chakrabarti · Tanmoy Chakraborty
Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment
Yin Zhao · minquan wang · Longjun Cai
Refined Learning Bounds for Kernel and Approximate $k$-Means
Yong Liu
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization
Bing-Jing Hsieh · Ping-Chun Hsieh · Xi Liu
Repulsive Deep Ensembles are Bayesian
Francesco D'Angelo · Vincent Fortuin
Re-ranking for image retrieval and transductive few-shot classification
Xi SHEN · Yang Xiao · Shell Xu Hu · Othman Sbai · Mathieu Aubry
Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence
Deng-Bao Wang · Lei Feng · Min-Ling Zhang
Rethinking gradient sparsification as total error minimization
Atal Sahu · Aritra Dutta · Ahmed M. Abdelmoniem · Trambak Banerjee · Marco Canini · Panos Kalnis
Rethinking the Variational Interpretation of Accelerated Optimization Methods
Peiyuan Zhang · Antonio Orvieto · Hadi Daneshmand
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
Wei Qiu · Xinrun Wang · Runsheng Yu · Rundong Wang · Xu He · Bo An · Svetlana Obraztsova · Zinovi Rabinovich
Robust Generalization despite Distribution Shift via Minimum Discriminating Information
Tobias Sutter · Andreas Krause · Daniel Kuhn
Robustifying Algorithms of Learning Latent Trees with Vector Variables
Fengzhuo Zhang · Vincent Tan
ROI Maximization in Stochastic Online Decision-Making
Nicolò Cesa-Bianchi · Tom Cesari · Yishay Mansour · Vianney Perchet
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
Kristopher Jensen · Ta-Chu Kao · Jasmine Stone · Guillaume Hennequin
ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers
Husheng Han · Kaidi Xu · Xing Hu · Xiaobing Chen · LING LIANG · Zidong Du · Qi Guo · Yanzhi Wang · Yunji Chen
Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels
Jizong Peng · Ping Wang · Christian Desrosiers · Marco Pedersoli
Self-Supervised Learning Disentangled Group Representation as Feature
Tan Wang · Zhongqi Yue · Jianqiang Huang · Qianru Sun · Hanwang Zhang
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks
Jesse Hagenaars · Federico Paredes-Valles · Guido de Croon
Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification
Tong Chen · Jean Lasserre · Victor Magron · Edouard Pauwels
Sim and Real: Better Together
Shirli Di-Castro · Dotan Di Castro · Shie Mannor
Solving Soft Clustering Ensemble via $k$-Sparse Discrete Wasserstein Barycenter
Ruizhe Qin · Mengying Li · Hu Ding
Sparse Spiking Gradient Descent
Nicolas Perez-Nieves · Dan Goodman
Spatial Ensemble: a Novel Model Smoothing Mechanism for Student-Teacher Framework
Tengteng Huang · Yifan Sun · Xun Wang · Haotian Yao · Chi Zhang
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution
Guangpin Tao · Xiaozhong Ji · Wenzhuo Wang · Shuo Chen · Chuming Lin · Yun Cao · Tong Lu · Donghao Luo · Ying Tai
Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay
Ruosi Wan · Zhanxing Zhu · Xiangyu Zhang · Jian Sun
SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection
Muhammad Akhtar Munir · Muhammad Haris Khan · M. Sarfraz · Mohsen Ali
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization
Fuchao Wei · Chenglong Bao · Yang Liu
Stochastic bandits with groups of similar arms.
Fabien Pesquerel · Hassan SABER · Odalric-Ambrym Maillard
Stochastic Multi-Armed Bandits with Control Variates
Arun Verma · Manjesh Kumar Hanawal
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret
Jean Tarbouriech · Runlong Zhou · Simon Du · Matteo Pirotta · Michal Valko · Alessandro Lazaric
STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization
Kfir Levy · Ali Kavis · Volkan Cevher
Structure-Aware Random Fourier Kernel for Graphs
Jinyuan Fang · Qiang Zhang · Zaiqiao Meng · Shangsong Liang
Stylized Dialogue Generation with Multi-Pass Dual Learning
Jinpeng Li · Yingce Xia · Rui Yan · Hongda Sun · Dongyan Zhao · Tie-Yan Liu
Subgoal Search For Complex Reasoning Tasks
Konrad Czechowski · Tomasz Odrzygóźdź · Marek Zbysiński · Michał Zawalski · Krzysztof Olejnik · Yuhuai Wu · Łukasz Kuciński · Piotr Miłoś
Subquadratic Overparameterization for Shallow Neural Networks
ChaeHwan Song · Ali Ramezani-Kebrya · Thomas Pethick · Armin Eftekhari · Volkan Cevher
SWAD: Domain Generalization by Seeking Flat Minima
Junbum Cha · Sanghyuk Chun · Kyungjae Lee · Han-Cheol Cho · Seunghyun Park · Yunsung Lee · Sungrae Park
Task-Adaptive Neural Network Search with Meta-Contrastive Learning
Wonyong Jeong · Hayeon Lee · Geon Park · Eunyoung Hyung · Jinheon Baek · Sung Ju Hwang
The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy
Masahiro Kato · Kenichiro McAlinn · Shota Yasui
The Complexity of Bayesian Network Learning: Revisiting the Superstructure
Robert Ganian · Viktoriia Korchemna
The Complexity of Sparse Tensor PCA
Davin Choo · Tommaso d'Orsi
The Implicit Bias of Minima Stability: A View from Function Space
Rotem Mulayoff · Tomer Michaeli · Daniel Soudry
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
Nathan Grinsztajn · Johan Ferret · Olivier Pietquin · philippe preux · Matthieu Geist
Time-independent Generalization Bounds for SGLD in Non-convex Settings
Tyler Farghly · Patrick Rebeschini
ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation
Guoqiang Wei · Cuiling Lan · Wenjun Zeng · Zhizheng Zhang · Zhibo Chen
Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective
Zhengzhuo Xu · Zenghao Chai · Chun Yuan
Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond
Risheng Liu · Yaohua Liu · Shangzhi Zeng · Jin Zhang
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach
Qitian Wu · Chenxiao Yang · Junchi Yan
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors
Zhaoqiang Liu · Subhroshekhar Ghosh · Jonathan Scarlett
Towards Understanding Why Lookahead Generalizes Better Than SGD and Beyond
Pan Zhou · Hanshu Yan · Xiaotong Yuan · Jiashi Feng · Shuicheng Yan
TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification
Shengcai Liao · Ling Shao
Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation
Qiming Hu · Xiaojie Guo
Truncated Marginal Neural Ratio Estimation
Benjamin K Miller · Alex Cole · Patrick Forré · Gilles Louppe · Christoph Weniger
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions
Huan Ma · Zongbo Han · Changqing Zhang · Huazhu Fu · Joey Tianyi Zhou · Qinghua Hu
Twins: Revisiting the Design of Spatial Attention in Vision Transformers
Xiangxiang Chu · Zhi Tian · Yuqing Wang · Bo Zhang · Haibing Ren · Xiaolin Wei · Huaxia Xia · Chunhua Shen
Uncertainty Calibration for Ensemble-Based Debiasing Methods
Ruibin Xiong · Yimeng Chen · Liang Pang · Xueqi Cheng · Zhi-Ming Ma · Yanyan Lan
Understanding Bandits with Graph Feedback
Houshuang Chen · zengfeng Huang · Shuai Li · Chihao Zhang
Understanding Partial Multi-Label Learning via Mutual Information
Xiuwen Gong · Dong Yuan · Wei Bao
Unlabeled Principal Component Analysis
Yunzhen Yao · Liangzu Peng · Manolis Tsakiris
Variational Bayesian Optimistic Sampling
Brendan O'Donoghue · Tor Lattimore
Variational Continual Bayesian Meta-Learning
Qiang Zhang · Jinyuan Fang · Zaiqiao Meng · Shangsong Liang · Emine Yilmaz
Variational Multi-Task Learning with Gumbel-Softmax Priors
Jiayi Shen · Xiantong Zhen · Marcel Worring · Ling Shao
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases
Shashi Kant Gupta · Mengmi Zhang · CHIA-CHIEN WU · Jeremy Wolfe · Gabriel Kreiman
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic
Yufeng Zhang · Siyu Chen · Zhuoran Yang · Michael Jordan · Zhaoran Wang
Well-tuned Simple Nets Excel on Tabular Datasets
Arlind Kadra · Marius Lindauer · Frank Hutter · Josif Grabocka
What’s a good imputation to predict with missing values?
Marine Le Morvan · Julie Josse · Erwan Scornet · Gael Varoquaux
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Social

Queer in AI

Claas Voelcker
5:00 AM - 7:00 AM

Queer in AI’s demographic survey reveals that most queer scientists in our community do not feel completely welcome in conferences and their work environments, with the main reasons being a lack of queer community and role models. Over the past years, Queer in AI has worked towards these goals, yet we have observed that the voices of marginalized queer communities - especially transgender, non-binary folks and queer BIPOC folks - have been neglected.

Our social is a safe and inclusive casual networking and socializing space for LGBTQIA+ individuals involved with AI that celebrates all queer people around the world. Together with our workshop, our events will create a community space where attendees can learn and grow from connecting with each other, bonding over shared experiences, and learning from each individual’s unique insights into AI, queerness, and beyond!

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Invited Talk
7:00 AM - 8:30 AM
Speaker Bio
Daniel Kahneman
Daniel Kahneman is Professor of Psychology and Public Affairs Emeritus at the Woodrow Wilson School, the Eugene Higgins Professor of Psychology Emeritus at Princeton University, and a fellow of the Center for Rationality at the Hebrew University in Jerusalem. Dr. Kahneman has held the position of professor of psychology at the Hebrew University in Jerusalem (1970-1978), the University of British Columbia (1978-1986), and the University of California, Berkeley (1986-1994). Dr. Kahneman is a member of the National Academy of Science, the Philosophical Society, the American Academy of Arts and Sciences and a fellow of the American Psychological Association, the American Psychological Society, the Society of Experimental Psychologists, and the Econometric Society. He has been the recipient of many awards, among them the Distinguished Scientific Contribution Award of the American Psychological Association (1982) and the Grawemeyer Prize (2002), both jointly with Amos Tversky, the Warren Medal of the Society of Experimental Psychologists (1995), the Hilgard Award for Career Contributions to General Psychology (1995), the Nobel Prize in Economic Sciences (2002), and the Lifetime Contribution Award of the American Psychological Association (2007). Dr. Kahneman holds honorary degrees from numerous Universities.
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Poster
8:30 AM - 10:00 AM
355 Events in this session
A/B Testing for Recommender Systems in a Two-sided Marketplace
Preetam Nandy · Divya Venugopalan · Chun Lo · Shaunak Chatterjee
A Central Limit Theorem for Differentially Private Query Answering
Jinshuo Dong · Weijie Su · Linjun Zhang
Analyzing the Confidentiality of Undistillable Teachers in Knowledge Distillation
Souvik Kundu · Qirui Sun · Yao Fu · Massoud Pedram · Peter Beerel
Boosted CVaR Classification
Runtian Zhai · Chen Dan · Arun Suggala · J. Zico Kolter · Pradeep Ravikumar
Breaking the centralized barrier for cross-device federated learning
Sai Praneeth Karimireddy · Martin Jaggi · Satyen Kale · Mehryar Mohri · Sashank Reddi · Sebastian Stich · Ananda Theertha Suresh
Conformal Bayesian Computation
Edwin Fong · Chris C Holmes
Curriculum Offline Imitating Learning
Minghuan Liu · Hanye Zhao · Zhengyu Yang · Jian Shen · Weinan Zhang · Li Zhao · Tie-Yan Liu
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning
Aodong Li · Alex Boyd · Padhraic Smyth · Stephan Mandt
Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess
Reid McIlroy-Young · Russell Wang · Siddhartha Sen · Jon Kleinberg · Ashton Anderson
Differentiable Unsupervised Feature Selection based on a Gated Laplacian
Ofir Lindenbaum · Uri Shaham · Erez Peterfreund · Jonathan Svirsky · Nicolas Casey · Yuval Kluger
Directed Probabilistic Watershed
Enrique Fita Sanmartin · Sebastian Damrich · Fred Hamprecht
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
Hermanni Hälvä · Sylvain Le Corff · Luc Lehéricy · Jonathan So · Yongjie Zhu · Elisabeth Gassiat · Aapo Hyvarinen
Distributed Saddle-Point Problems Under Data Similarity
Aleksandr Beznosikov · Gesualdo Scutari · Alexander Rogozin · Alexander Gasnikov
DRIVE: One-bit Distributed Mean Estimation
Shay Vargaftik · Ran Ben-Basat · Amit Portnoy · Gal Mendelson · Yaniv Ben-Itzhak · Michael Mitzenmacher
Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging
Ali Hashemi · Yijing Gao · Chang Cai · Sanjay Ghosh · Klaus-Robert Müller · Srikantan Nagarajan · Stefan Haufe
End-to-End Weak Supervision
Salva Rühling Cachay · Benedikt Boecking · Artur Dubrawski
Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms
Jeremy Seeman · Matthew Reimherr · Aleksandra Slavković
Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality
Stefanos Leonardos · Georgios Piliouras · Kelly Spendlove
Fair Classification with Adversarial Perturbations
L. Elisa Celis · Anay Mehrotra · Nisheeth Vishnoi
Faster Neural Network Training with Approximate Tensor Operations
Menachem Adelman · Kfir Levy · Ido Hakimi · Mark Silberstein
Fast Pure Exploration via Frank-Wolfe
Po-An Wang · Ruo-Chun Tzeng · Alexandre Proutiere
Federated Multi-Task Learning under a Mixture of Distributions
Othmane Marfoq · Giovanni Neglia · Aurélien Bellet · Laetitia Kameni · Richard Vidal
Good Classification Measures and How to Find Them
Martijn Gösgens · Anton Zhiyanov · Aleksey Tikhonov · Liudmila Prokhorenkova
Group Equivariant Subsampling
Jin Xu · Hyunjik Kim · Thomas Rainforth · Yee Teh
Joint Inference for Neural Network Depth and Dropout Regularization
Kishan K C · Rui Li · MohammadMahdi Gilany
Lattice partition recovery with dyadic CART
OSCAR HERNAN MADRID PADILLA · Yi Yu · Alessandro Rinaldo
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions
Bruno Loureiro · Gabriele Sicuro · Cedric Gerbelot · Alessandro Pacco · Florent Krzakala · Lenka Zdeborová
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs
Tao Liu · Ruida Zhou · Dileep Kalathil · P. R. Kumar · Chao Tian
Learning to Synthesize Programs as Interpretable and Generalizable Policies
Dweep Trivedi · Jesse Zhang · Shao-Hua Sun · Joseph Lim
Least Square Calibration for Peer Reviews
Sijun Tan · Jibang Wu · Xiaohui Bei · Haifeng Xu
Local policy search with Bayesian optimization
Sarah Müller · Alexander von Rohr · Sebastian Trimpe
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition
Mark Boss · Varun Jampani · Raphael Braun · Ce Liu · Jonathan Barron · Hendrik PA Lensch
Neural Trees for Learning on Graphs
Rajat Talak · Siyi Hu · Lisa Peng · Luca Carlone
Noisy Recurrent Neural Networks
Soon Hoe Lim · N. Benjamin Erichson · Liam Hodgkinson · Michael Mahoney
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay
Ekaterina Lobacheva · Maxim Kodryan · Nadezhda Chirkova · Andrey Malinin · Dmitry Vetrov
Outcome-Driven Reinforcement Learning via Variational Inference
Tim G. J. Rudner · Vitchyr Pong · Rowan McAllister · Yarin Gal · Sergey Levine
Probabilistic Attention for Interactive Segmentation
Prasad Gabbur · Manjot Bilkhu · Javier Movellan
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition
Jiaming Liu · Salman Asif · Brendt Wohlberg · Ulugbek Kamilov
Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification
Walter Gerych · Thomas Hartvigsen · Luke Buquicchio · Emmanuel Agu · Elke A. Rundensteiner
Retiring Adult: New Datasets for Fair Machine Learning
Frances Ding · Moritz Hardt · John Miller · Ludwig Schmidt
Risk-Aware Transfer in Reinforcement Learning using Successor Features
Michael Gimelfarb · Andre Barreto · Scott Sanner · Chi-Guhn Lee
Robust and Decomposable Average Precision for Image Retrieval
Elias Ramzi · Nicolas THOME · Clément Rambour · Nicolas Audebert · Xavier Bitot
Roto-translated Local Coordinate Frames For Interacting Dynamical Systems
Miltiadis Kofinas · Naveen Nagaraja · Stratis Gavves
Scalable Inference of Sparsely-changing Gaussian Markov Random Fields
Salar Fattahi · Andres Gomez
Settling the Variance of Multi-Agent Policy Gradients
Jakub Grudzien Kuba · Muning Wen · Linghui Meng · shangding gu · Haifeng Zhang · David Mguni · Jun Wang · Yaodong Yang
Shape As Points: A Differentiable Poisson Solver
Songyou Peng · Chiyu Jiang · Yiyi Liao · Michael Niemeyer · Marc Pollefeys · Andreas Geiger
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation
Can Qin · Handong Zhao · Lichen Wang · Huan Wang · Yulun Zhang · Yun Fu
TAAC: Temporally Abstract Actor-Critic for Continuous Control
Haonan Yu · Wei Xu · Haichao Zhang
TokenLearner: Adaptive Space-Time Tokenization for Videos
Michael Ryoo · AJ Piergiovanni · Anurag Arnab · Mostafa Dehghani · Anelia Angelova
Topological Attention for Time Series Forecasting
Sebastian Zeng · Florian Graf · Christoph Hofer · Roland Kwitt
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
Kento Nozawa · Issei Sato
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
Harshavardhan Kamarthi · Lingkai Kong · Alexander Rodriguez · Chao Zhang · B. Aditya Prakash
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation
Lin Guan · Mudit Verma · Sihang Guo · Ruohan Zhang · Subbarao Kambhampati
XCiT: Cross-Covariance Image Transformers
Alaaeldin Ali · Hugo Touvron · Mathilde Caron · Piotr Bojanowski · Matthijs Douze · Armand Joulin · Ivan Laptev · Natalia Neverova · Gabriel Synnaeve · Jakob Verbeek · Herve Jegou
$(\textrm{Implicit})^2$: Implicit Layers for Implicit Representations
Zhichun Huang · Shaojie Bai · J. Zico Kolter
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks
Itay Hubara · Brian Chmiel · Moshe Island · Ron Banner · Joseph Naor · Daniel Soudry
Adapting to function difficulty and growth conditions in private optimization
Hilal Asi · Daniel Levy · John Duchi
Adaptive Conformal Inference Under Distribution Shift
Isaac Gibbs · Emmanuel Candes
Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback
Hang Wang · Sen Lin · Junshan Zhang
Adaptive Machine Unlearning
Varun Gupta · Christopher Jung · Seth Neel · Aaron Roth · Saeed Sharifi-Malvajerdi · Chris Waites
Adaptive Sampling for Minimax Fair Classification
Shubhanshu Shekhar · Greg Fields · Mohammad Ghavamzadeh · Tara Javidi
Adjusting for Autocorrelated Errors in Neural Networks for Time Series
Fan-Keng Sun · Chris Lang · Duane Boning
Adversarial Neuron Pruning Purifies Backdoored Deep Models
Dongxian Wu · Yisen Wang
Adversarial Regression with Doubly Non-negative Weighting Matrices
Tam Le · Truyen Nguyen · Makoto Yamada · Jose Blanchet · Viet Anh Nguyen
A Gang of Adversarial Bandits
Mark Herbster · Stephen Pasteris · Fabio Vitale · Massimiliano Pontil
A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition
Junjiao Tian · Dylan Yung · Yen-Chang Hsu · Zsolt Kira
Aligned Structured Sparsity Learning for Efficient Image Super-Resolution
Yulun Zhang · Huan Wang · Can Qin · Yun Fu
(Almost) Free Incentivized Exploration from Decentralized Learning Agents
Chengshuai Shi · Haifeng Xu · Wei Xiong · Cong Shen
Analytic Insights into Structure and Rank of Neural Network Hessian Maps
Sidak Pal Singh · Gregor Bachmann · Thomas Hofmann
A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models
Ibrahim Alabdulmohsin · Mario Lucic
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum
Prashant Khanduri · Siliang Zeng · Mingyi Hong · Hoi-To Wai · Zhaoran Wang · Zhuoran Yang
An online passive-aggressive algorithm for difference-of-squares classification
Lawrence Saul
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis
Zihang Meng · Rudrasis Chakraborty · Vikas Singh
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components
Nate Veldt · Austin Benson · Jon Kleinberg
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training
Shangshu Qian · Viet Hung Pham · Thibaud Lutellier · Zeou Hu · Jungwon Kim · Lin Tan · Yaoliang Yu · Jiahao Chen · Sameena Shah
Are Transformers more robust than CNNs?
Yutong Bai · Jieru Mei · Alan Yuille · Cihang Xie
Assessing Fairness in the Presence of Missing Data
Yiliang Zhang · Qi Long
Asymptotics of representation learning in finite Bayesian neural networks
Jacob Zavatone-Veth · Abdulkadir Canatar · Ben Ruben · Cengiz Pehlevan
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection
Morgane Austern · Vasilis Syrgkanis
Asynchronous Stochastic Optimization Robust to Arbitrary Delays
Alon Cohen · Amit Daniely · Yoel Drori · Tomer Koren · Mariano Schain
ATISS: Autoregressive Transformers for Indoor Scene Synthesis
Despoina Paschalidou · Amlan Kar · Maria Shugrina · Karsten Kreis · Andreas Geiger · Sanja Fidler
Attention over Learned Object Embeddings Enables Complex Visual Reasoning
David Ding · Felix Hill · Adam Santoro · Malcolm Reynolds · Matt Botvinick
A Unified Approach to Fair Online Learning via Blackwell Approachability
Evgenii Chzhen · Christophe Giraud · Gilles Stoltz
Automatic Symmetry Discovery with Lie Algebra Convolutional Network
Nima Dehmamy · Robin Walters · Yanchen Liu · Dashun Wang · Rose Yu
Average-Reward Learning and Planning with Options
Yi Wan · Abhishek Naik · Rich Sutton
Bayesian Bellman Operators
Mattie Fellows · Kristian Hartikainen · Shimon Whiteson
Beltrami Flow and Neural Diffusion on Graphs
Benjamin Chamberlain · James Rowbottom · Davide Eynard · Francesco Di Giovanni · Xiaowen Dong · Michael Bronstein
Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation
Ke Wang · Vidya Muthukumar · Christos Thrampoulidis
Best-case lower bounds in online learning
Cristóbal Guzmán · Nishant Mehta · Ali Mortazavi
Beyond Bandit Feedback in Online Multiclass Classification
Dirk van der Hoeven · Federico Fusco · Nicolò Cesa-Bianchi
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
Youngseog Chung · Willie Neiswanger · Ian Char · Jeff Schneider
Boosting with Multiple Sources
Corinna Cortes · Mehryar Mohri · Dmitry Storcheus · Ananda Theertha Suresh
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
Xiao Jin · Pin-Yu Chen · Chia-Yi Hsu · Chia-Mu Yu · Tianyi Chen
CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks
Sakshi Varshney · Vinay Kumar Verma · P. K. Srijith · Lawrence Carin · Piyush Rai
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial
Yang Liu · Jialu Wang
Can multi-label classification networks know what they don’t know?
Haoran Wang · Weitang Liu · Alex Bocchieri · Yixuan Li
Canonical Capsules: Self-Supervised Capsules in Canonical Pose
Weiwei Sun · Andrea Tagliasacchi · Boyang Deng · Sara Sabour · Soroosh Yazdani · Geoffrey Hinton · Kwang Moo Yi
Causal Influence Detection for Improving Efficiency in Reinforcement Learning
Maximilian Seitzer · Bernhard Schölkopf · Georg Martius
Causal Navigation by Continuous-time Neural Networks
Charles Vorbach · Ramin Hasani · Alexander Amini · Mathias Lechner · Daniela Rus
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
Timo Milbich · Karsten Roth · Samarth Sinha · Ludwig Schmidt · Marzyeh Ghassemi · Bjorn Ommer
Characterizing the risk of fairwashing
Ulrich Aïvodji · Hiromi Arai · Sébastien Gambs · Satoshi Hara
Circa: Stochastic ReLUs for Private Deep Learning
Zahra Ghodsi · Nandan Kumar Jha · Brandon Reagen · Siddharth Garg
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation
Yuchao Qin · Fergus Imrie · Alihan Hüyük · Daniel Jarrett · alexander gimson · Mihaela van der Schaar
CogView: Mastering Text-to-Image Generation via Transformers
Ming Ding · Zhuoyi Yang · Wenyi Hong · Wendi Zheng · Chang Zhou · Da Yin · Junyang Lin · Xu Zou · Zhou Shao · Hongxia Yang · Jie Tang
Collaborating with Humans without Human Data
DJ Strouse · Kevin McKee · Matt Botvinick · Edward Hughes · Richard Everett
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration
Gavin Kerrigan · Padhraic Smyth · Mark Steyvers
Compositional Transformers for Scene Generation
Dor Arad Hudson · Larry Zitnick
Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers
Tommaso d'Orsi · Chih-Hung Liu · Rajai Nasser · Gleb Novikov · David Steurer · Stefan Tiegel
Container: Context Aggregation Networks
peng gao · Jiasen Lu · Hongsheng Li · Roozbeh Mottaghi · Aniruddha Kembhavi
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
Sreenivas Gollapudi · Guru Guruganesh · Kostas Kollias · Pasin Manurangsi · Renato Leme · Jon Schneider
Continual Auxiliary Task Learning
Matthew McLeod · Chunlok Lo · Matthew Schlegel · Andrew Jacobsen · Raksha Kumaraswamy · Martha White · Adam White
Contrastive Reinforcement Learning of Symbolic Reasoning Domains
Gabriel Poesia · WenXin Dong · Noah Goodman
Controllable and Compositional Generation with Latent-Space Energy-Based Models
Weili Nie · Arash Vahdat · Anima Anandkumar
Coresets for Decision Trees of Signals
Ibrahim Jubran · Ernesto Evgeniy Sanches Shayda · Ilan I Newman · Dan Feldman
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
Gavin Brown · Marco Gaboardi · Adam Smith · Jonathan Ullman · Lydia Zakynthinou
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation
Abhishek Sinha · Jiaming Song · Chenlin Meng · Stefano Ermon
Data driven semi-supervised learning
Maria-Florina Balcan · Dravyansh Sharma
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
Boris van Breugel · Trent Kyono · Jeroen Berrevoets · Mihaela van der Schaar
Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen · Kevin Lu · Aravind Rajeswaran · Kimin Lee · Aditya Grover · Misha Laskin · Pieter Abbeel · Aravind Srinivas · Igor Mordatch
Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP
Josh Rozner · Christopher Potts · Kyle Mahowald
Deep Explicit Duration Switching Models for Time Series
Abdul Fatir Ansari · Konstantinos Benidis · Richard Kurle · Ali Caner Turkmen · Harold Soh · Alexander Smola · Bernie Wang · Tim Januschowski
Deep Learning on a Data Diet: Finding Important Examples Early in Training
Mansheej Paul · Surya Ganguli · Gintare Karolina Dziugaite
Deep Learning with Label Differential Privacy
Badih Ghazi · Noah Golowich · Ravi Kumar · Pasin Manurangsi · Chiyuan Zhang
Deep Networks Provably Classify Data on Curves
Tingran Wang · Sam Buchanan · Dar Gilboa · John Wright
Dense Keypoints via Multiview Supervision
Zhixuan Yu · Haozheng Yu · Long Sha · Sujoy Ganguly · Hyun Soo Park
Differentiable Multiple Shooting Layers
Stefano Massaroli · Michael Poli · Sho Sonoda · Taiji Suzuki · Jinkyoo Park · Atsushi Yamashita · Hajime Asama
Differentially Private Empirical Risk Minimization under the Fairness Lens
Cuong Tran · My Dinh · Ferdinando Fioretto
Differentially Private n-gram Extraction
Kunho Kim · Sivakanth Gopi · Janardhan Kulkarni · Sergey Yekhanin
Differentially Private Sampling from Distributions
Sofya Raskhodnikova · Satchit Sivakumar · Adam Smith · Marika Swanberg
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings
Raef Bassily · Cristóbal Guzmán · Michael Menart
Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent
Rishav Chourasia · Jiayuan Ye · Reza Shokri
Disrupting Deep Uncertainty Estimation Without Harming Accuracy
Ido Galil · Ran El-Yaniv
Distributed Machine Learning with Sparse Heterogeneous Data
Dominic Richards · Sahand Negahban · Patrick Rebeschini
Distributed Principal Component Analysis with Limited Communication
Foivos Alimisis · Peter Davies · Bart Vandereycken · Dan Alistarh
Distribution-free inference for regression: discrete, continuous, and in between
Yonghoon Lee · Rina Barber
Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Tianshi Cao · Alex Bie · Arash Vahdat · Sanja Fidler · Karsten Kreis
Double/Debiased Machine Learning for Dynamic Treatment Effects
Greg Lewis · Vasilis Syrgkanis
Dr Jekyll & Mr Hyde: the strange case of off-policy policy updates
Romain Laroche · Remi Tachet des Combes
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers
Mikita Dvornik · Isma Hadji · Konstantinos Derpanis · Animesh Garg · Allan Jepson
Dynamic influence maximization
Binghui Peng
EditGAN: High-Precision Semantic Image Editing
Huan Ling · Karsten Kreis · Daiqing Li · Seung Wook Kim · Antonio Torralba · Sanja Fidler
Efficient Training of Retrieval Models using Negative Cache
Erik Lindgren · Sashank Reddi · Ruiqi Guo · Sanjiv Kumar
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization
Pranav Subramani · Nicholas Vadivelu · Gautam Kamath
Ensembling Graph Predictions for AMR Parsing
Thanh Lam Hoang · Gabriele Picco · Yufang Hou · Young-Suk Lee · Lam Nguyen · Dzung Phan · Vanessa Lopez · Ramon Fernandez Astudillo
Equilibrium Refinement for the Age of Machines: The One-Sided Quasi-Perfect Equilibrium
Gabriele Farina · Tuomas Sandholm
Estimating the Long-Term Effects of Novel Treatments
Keith Battocchi · Eleanor Dillon · Maggie Hei · Greg Lewis · Miruna Oprescu · Vasilis Syrgkanis
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Yangsibo Huang · Samyak Gupta · Zhao Song · Kai Li · Sanjeev Arora
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi
Ho Chit Siu · Jaime Peña · Edenna Chen · Yutai Zhou · Victor Lopez · Kyle Palko · Kimberlee Chang · Ross Allen
Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks
Aran Nayebi · Alexander Attinger · Malcolm Campbell · Kiah Hardcastle · Isabel Low · Caitlin S Mallory · Gabriel Mel · Ben Sorscher · Alex H Williams · Surya Ganguli · Lisa Giocomo · Dan Yamins
Explicit loss asymptotics in the gradient descent training of neural networks
Maksim Velikanov · Dmitry Yarotsky
Exploiting Opponents Under Utility Constraints in Sequential Games
Martino Bernasconi · Federico Cacciamani · Simone Fioravanti · Nicola Gatti · Alberto Marchesi · Francesco Trovò
Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing
Yan-Bo Lin · Hung-Yu Tseng · Hsin-Ying Lee · Yen-Yu Lin · Ming-Hsuan Yang
Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling
Chen Tang · Wei Zhan · Masayoshi Tomizuka
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models
Yaofeng Desmond Zhong · Biswadip Dey · Amit Chakraborty
FACMAC: Factored Multi-Agent Centralised Policy Gradients
Bei Peng · Tabish Rashid · Christian Schroeder de Witt · Pierre-Alexandre Kamienny · Philip Torr · Wendelin Boehmer · Shimon Whiteson
Fair Algorithms for Multi-Agent Multi-Armed Bandits
Safwan Hossain · Evi Micha · Nisarg Shah
Fair Clustering Under a Bounded Cost
Seyed Esmaeili · Brian Brubach · Aravind Srinivasan · John Dickerson
Fair Scheduling for Time-dependent Resources
Bo Li · Minming Li · Ruilong Zhang
Fair Sortition Made Transparent
Bailey Flanigan · Gregory Kehne · Ariel Procaccia
Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem
Adarsh Barik · Jean Honorio
Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu · Sivakanth Gopi · Janardhan Kulkarni · Yin Tat Lee · Judy Hanwen Shen · Uthaipon Tantipongpipat
Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes
Mohamad Amin Sharifi Kolarijani · Gyula Max · Peyman Mohajerin Esfahani
Faster proximal algorithms for matrix optimization using Jacobi-based eigenvalue methods
Hamza Fawzi · Harry Goulbourne
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification
Jiong Zhang · Wei-Cheng Chang · Hsiang-Fu Yu · Inderjit Dhillon
Federated-EM with heterogeneity mitigation and variance reduction
Aymeric Dieuleveut · Gersende Fort · Eric Moulines · Geneviève Robin
Federated Graph Classification over Non-IID Graphs
Han Xie · Jing Ma · Li Xiong · Carl Yang
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Mikhail Khodak · Renbo Tu · Tian Li · Liam Li · Maria-Florina Balcan · Virginia Smith · Ameet Talwalkar
Federated Reconstruction: Partially Local Federated Learning
Karan Singhal · Hakim Sidahmed · Zachary Garrett · Shanshan Wu · John Rush · Sushant Prakash
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
Ruihan Wu · Chuan Guo · Awni Hannun · Laurens van der Maaten
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Samuel Horváth · Stefanos Laskaridis · Mario Almeida · Ilias Leontiadis · Stylianos Venieris · Nicholas Lane
FLEX: Unifying Evaluation for Few-Shot NLP
Jonathan Bragg · Arman Cohan · Kyle Lo · Iz Beltagy
Framing RNN as a kernel method: A neural ODE approach
Adeline Fermanian · Pierre Marion · Jean-Philippe Vert · Gérard Biau
Functional Variational Inference based on Stochastic Process Generators
Chao Ma · José Miguel Hernández-Lobato
General Nonlinearities in SO(2)-Equivariant CNNs
Daniel Franzen · Michael Wand
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement
Martin Engelcke · Oiwi Parker Jones · Ingmar Posner
Glance-and-Gaze Vision Transformer
Qihang Yu · Yingda Xia · Yutong Bai · Yongyi Lu · Alan Yuille · Wei Shen
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators
Yunhui Long · Boxin Wang · Zhuolin Yang · Bhavya Kailkhura · Aston Zhang · Carl Gunter · Bo Li
Gradient Starvation: A Learning Proclivity in Neural Networks
Mohammad Pezeshki · Oumar Kaba · Yoshua Bengio · Aaron Courville · Doina Precup · Guillaume Lajoie
Grammar-Based Grounded Lexicon Learning
Jiayuan Mao · Freda Shi · Jiajun Wu · Roger Levy · Josh Tenenbaum
Heavy Ball Momentum for Conditional Gradient
Bingcong Li · Alireza Sadeghi · Georgios Giannakis
Hierarchical Reinforcement Learning with Timed Subgoals
Nico Gürtler · Dieter Büchler · Georg Martius
How to transfer algorithmic reasoning knowledge to learn new algorithms?
Louis-Pascal Xhonneux · Andreea-Ioana Deac · Petar Veličković · Jian Tang
Hyperbolic Busemann Learning with Ideal Prototypes
Mina Ghadimi Atigh · Martin Keller-Ressel · Pascal Mettes
Hyperparameter Optimization Is Deceiving Us, and How to Stop It
A. Feder Cooper · Yucheng Lu · Jessica Forde · Christopher De Sa
Implicit Generative Copulas
Tim Janke · Mohamed Ghanmi · Florian Steinke
Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes
Brian Brubach · Nathaniel Grammel · Will Ma · Aravind Srinivasan
Improved Regret Bounds for Tracking Experts with Memory
James Robinson · Mark Herbster
Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss
Michael Iuzzolino · Michael Mozer · Samy Bengio
Improving Compositionality of Neural Networks by Decoding Representations to Inputs
Mike Wu · Noah Goodman · Stefano Ermon
Improving Conditional Coverage via Orthogonal Quantile Regression
Shai Feldman · Stephen Bates · Yaniv Romano
Individual Privacy Accounting via a Rényi Filter
Vitaly Feldman · Tijana Zrnic
Instance-Conditioned GAN
Arantxa Casanova · Marlene Careil · Jakob Verbeek · Michal Drozdzal · Adriana Romero Soriano
Interesting Object, Curious Agent: Learning Task-Agnostic Exploration
Simone Parisi · Victoria Dean · Deepak Pathak · Abhinav Gupta
Inverse Problems Leveraging Pre-trained Contrastive Representations
Sriram Ravula · Georgios Smyrnis · Matt Jordan · Alex Dimakis
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods
Terrance Liu · Giuseppe Vietri · Steven Wu
Label Disentanglement in Partition-based Extreme Multilabel Classification
Xuanqing Liu · Wei-Cheng Chang · Hsiang-Fu Yu · Cho-Jui Hsieh · Inderjit Dhillon
Label-Imbalanced and Group-Sensitive Classification under Overparameterization
Ganesh Ramachandra Kini · Orestis Paraskevas · Samet Oymak · Christos Thrampoulidis
Label Noise SGD Provably Prefers Flat Global Minimizers
Alex Damian · Tengyu Ma · Jason Lee
Landscape analysis of an improved power method for tensor decomposition
Joe Kileel · Timo Klock · João M Pereira
Last-iterate Convergence in Extensive-Form Games
Chung-Wei Lee · Christian Kroer · Haipeng Luo
LEADS: Learning Dynamical Systems that Generalize Across Environments
Yuan Yin · Ibrahim Ayed · Emmanuel de Bézenac · Nicolas Baskiotis · Patrick Gallinari
Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds
Antonios Antoniadis · Christian Coester · Marek Elias · Adam Polak · Bertrand Simon
Learning Hard Optimization Problems: A Data Generation Perspective
James Kotary · Ferdinando Fioretto · Pascal Van Hentenryck
Learning in Non-Cooperative Configurable Markov Decision Processes
Giorgia Ramponi · Alberto Maria Metelli · Alessandro Concetti · Marcello Restelli
Learning in two-player zero-sum partially observable Markov games with perfect recall
Tadashi Kozuno · Pierre Ménard · Remi Munos · Michal Valko
Learning Markov State Abstractions for Deep Reinforcement Learning
Cameron Allen · Neev Parikh · Omer Gottesman · George Konidaris
Learning Semantic Representations to Verify Hardware Designs
Shobha Vasudevan · Wenjie (Joe) Jiang · David Bieber · Rishabh Singh · hamid shojaei · C. Richard Ho · Charles Sutton
Learning to Generate Visual Questions with Noisy Supervision
Shen Kai · Lingfei Wu · Siliang Tang · Yueting Zhuang · zhen he · Zhuoye Ding · Yun Xiao · Bo Long
Learning to Predict Trustworthiness with Steep Slope Loss
Yan Luo · Yongkang Wong · Mohan Kankanhalli · Qi Zhao
Learning to Schedule Heuristics in Branch and Bound
Antonia Chmiela · Elias Khalil · Ambros Gleixner · Andrea Lodi · Sebastian Pokutta
Learning to See by Looking at Noise
Manel Baradad Jurjo · Jonas Wulff · Tongzhou Wang · Phillip Isola · Antonio Torralba
Learning with User-Level Privacy
Daniel Levy · Ziteng Sun · Kareem Amin · Satyen Kale · Alex Kulesza · Mehryar Mohri · Ananda Theertha Suresh
Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
Vincent Sitzmann · Semon Rezchikov · Bill Freeman · Josh Tenenbaum · Fredo Durand
Linear and Kernel Classification in the Streaming Model: Improved Bounds for Heavy Hitters
Arvind Mahankali · David Woodruff
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
Aritra Mitra · Rayana Jaafar · George J. Pappas · Hamed Hassani
Littlestone Classes are Privately Online Learnable
Noah Golowich · Roi Livni
Local Differential Privacy for Regret Minimization in Reinforcement Learning
Evrard Garcelon · Vianney Perchet · Ciara Pike-Burke · Matteo Pirotta
Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization
Travers Rhodes · Daniel Lee
Local Hyper-Flow Diffusion
Kimon Fountoulakis · Pan Li · Shenghao Yang
Locally differentially private estimation of functionals of discrete distributions
Cristina Butucea · Yann ISSARTEL
Locally private online change point detection
Tom Berrett · Yi Yu
Logarithmic Regret from Sublinear Hints
Aditya Bhaskara · Ashok Cutkosky · Ravi Kumar · Manish Purohit
Logarithmic Regret in Feature-based Dynamic Pricing
Jianyu Xu · Yu-Xiang Wang
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
Thomas FEL · Remi Cadene · Mathieu Chalvidal · Matthieu Cord · David Vigouroux · Thomas Serre
Machine Learning for Variance Reduction in Online Experiments
Yongyi Guo · Dominic Coey · Mikael Konutgan · Wenting Li · Chris Schoener · Matt Goldman
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications
Leonard Berrada · Sumanth Dathathri · Krishnamurthy Dvijotham · Robert Stanforth · Rudy Bunel · Jonathan Uesato · Sven Gowal · M. Pawan Kumar
Margin-Independent Online Multiclass Learning via Convex Geometry
Guru Guruganesh · Allen Liu · Jon Schneider · Joshua Wang
MCMC Variational Inference via Uncorrected Hamiltonian Annealing
Tomas Geffner · Justin Domke
MERLOT: Multimodal Neural Script Knowledge Models
Rowan Zellers · Ximing Lu · Jack Hessel · Youngjae Yu · Jae Sung Park · Jize Cao · Ali Farhadi · Yejin Choi
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers
Jeffrey Negrea · Blair Bilodeau · Nicolò Campolongo · Francesco Orabona · Dan Roy
Minimizing Polarization and Disagreement in Social Networks via Link Recommendation
Liwang Zhu · Qi Bao · Zhongzhi Zhang
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage
Jonathan Chang · Masatoshi Uehara · Dhruv Sreenivas · Rahul Kidambi · Wen Sun
Mixability made efficient: Fast online multiclass logistic regression
Rémi Jézéquel · Pierre Gaillard · Alessandro Rudi
Mixture Proportion Estimation and PU Learning:A Modern Approach
Saurabh Garg · Yifan Wu · Alexander Smola · Sivaraman Balakrishnan · Zachary Lipton
Mixture weights optimisation for Alpha-Divergence Variational Inference
Kamélia Daudel · randal douc
Multiclass Boosting and the Cost of Weak Learning
Nataly Brukhim · Elad Hazan · Shay Moran · Indraneel Mukherjee · Robert Schapire
Multiclass versus Binary Differentially Private PAC Learning
Satchit Sivakumar · Mark Bun · Marco Gaboardi
Multimodal Few-Shot Learning with Frozen Language Models
Maria Tsimpoukelli · Jacob L Menick · Serkan Cabi · S. M. Ali Eslami · Oriol Vinyals · Felix Hill
Multi-Scale Representation Learning on Proteins
Vignesh Ram Somnath · Charlotte Bunne · Andreas Krause
Near-Optimal No-Regret Learning in General Games
Constantinos Daskalakis · Maxwell Fishelson · Noah Golowich
NeRV: Neural Representations for Videos
Hao Chen · Bo He · Hanyu Wang · Yixuan Ren · Ser Nam Lim · Abhinav Shrivastava
Neural Algorithmic Reasoners are Implicit Planners
Andreea-Ioana Deac · Petar Veličković · Ognjen Milinkovic · Pierre-Luc Bacon · Jian Tang · Mladen Nikolic
Neural Bootstrapper
Minsuk Shin · Hyungjoo Cho · Hyun-seok Min · Sungbin Lim
Neural Distance Embeddings for Biological Sequences
Gabriele Corso · Zhitao Ying · Michal Pándy · Petar Veličković · Jure Leskovec · Pietro Liò
Neural Tangent Kernel Maximum Mean Discrepancy
Xiuyuan Cheng · Yao Xie
NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem
Liang Xin · Wen Song · Zhiguang Cao · Jie Zhang
NeuroMLR: Robust & Reliable Route Recommendation on Road Networks
Jayant Jain · Vrittika Bagadia · Sahil Manchanda · Sayan Ranu
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
Peng Wang · Lingjie Liu · Yuan Liu · Christian Theobalt · Taku Komura · Wenping Wang
Never Go Full Batch (in Stochastic Convex Optimization)
Idan Amir · Yair Carmon · Tomer Koren · Roi Livni
Non-asymptotic Error Bounds for Bidirectional GANs
Shiao Liu · Yunfei Yang · Jian Huang · Yuling Jiao · Yang Wang
Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data
HaiYing Wang · Aonan Zhang · Chong Wang
NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM
Connor Holmes · Minjia Zhang · Yuxiong He · Bo Wu
Object-Centric Representation Learning with Generative Spatial-Temporal Factorization
Nanbo Li · Muhammad Ahmed Raza · Wenbin Hu · Zhaole Sun · Robert Fisher
Offline Meta Reinforcement Learning -- Identifiability Challenges and Effective Data Collection Strategies
Ron Dorfman · Idan Shenfeld · Aviv Tamar
On Large-Cohort Training for Federated Learning
Zachary Charles · Zachary Garrett · Zhouyuan Huo · Sergei Shmulyian · Virginia Smith
Online Adaptation to Label Distribution Shift
Ruihan Wu · Chuan Guo · Yi Su · Kilian Weinberger
Online Control of Unknown Time-Varying Dynamical Systems
Edgar Minasyan · Paula Gradu · Max Simchowitz · Elad Hazan
Online false discovery rate control for anomaly detection in time series
Quentin Rebjock · Baris Kurt · Tim Januschowski · Laurent Callot
Online Knapsack with Frequency Predictions
Sungjin Im · Ravi Kumar · Mahshid Montazer Qaem · Manish Purohit
Online Market Equilibrium with Application to Fair Division
Yuan Gao · Alex Peysakhovich · Christian Kroer
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
Rui Huang · Andrew Geng · Yixuan Li
On the interplay between data structure and loss function in classification problems
Stéphane d'Ascoli · Marylou Gabrié · Levent Sagun · Giulio Biroli
On the Out-of-distribution Generalization of Probabilistic Image Modelling
Mingtian Zhang · Andi Zhang · Steven McDonagh
On the Sample Complexity of Learning under Geometric Stability
Alberto Bietti · Luca Venturi · Joan Bruna
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles
Menachem Sadigurschi · Uri Stemmer
On The Structure of Parametric Tournaments with Application to Ranking from Pairwise Comparisons
Vishnu Veerathu · Arun Rajkumar
On the Theory of Reinforcement Learning with Once-per-Episode Feedback
Niladri Chatterji · Aldo Pacchiano · Peter Bartlett · Michael Jordan
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization
Kuniaki Saito · Donghyun Kim · Kate Saenko
Optimality and Stability in Federated Learning: A Game-theoretic Approach
Kate Donahue · Jon Kleinberg
Optimal Rates for Random Order Online Optimization
Uri Sherman · Tomer Koren · Yishay Mansour
Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings
Ming Yin · Yu-Xiang Wang
Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure
Aviv Rosenberg · Yishay Mansour
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning
Vivien Cabannes · Loucas Pillaud-Vivien · Francis Bach · Alessandro Rudi
Parameter Inference with Bifurcation Diagrams
Gregory Szep · Neil Dalchau · Attila Csikász-Nagy
Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling
Hongyu Gong · Yun Tang · Juan Pino · Xian Li
PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations
Moshe Eliasof · Eldad Haber · Eran Treister
Periodic Activation Functions Induce Stationarity
Lassi Meronen · Martin Trapp · Arno Solin
Perturb-and-max-product: Sampling and learning in discrete energy-based models
Miguel Lazaro-Gredilla · Antoine Dedieu · Dileep George
PettingZoo: Gym for Multi-Agent Reinforcement Learning
J Terry · Benjamin Black · Nathaniel Grammel · Mario Jayakumar · Ananth Hari · Ryan Sullivan · Luis S Santos · Clemens Dieffendahl · Caroline Horsch · Rodrigo Perez-Vicente · Niall Williams · Yashas Lokesh · Praveen Ravi
Photonic Differential Privacy with Direct Feedback Alignment
Ruben Ohana · Hamlet Medina · Julien Launay · Alessandro Cappelli · Iacopo Poli · Liva Ralaivola · Alain Rakotomamonjy
Pipeline Combinators for Gradual AutoML
Guillaume Baudart · Martin Hirzel · Kiran Kate · Parikshit Ram · Avi Shinnar · Jason Tsay
Planning from Pixels in Environments with Combinatorially Hard Search Spaces
Marco Bagatella · Miroslav Olšák · Michal Rolínek · Georg Martius
Post-processing for Individual Fairness
Felix Petersen · Debarghya Mukherjee · Yuekai Sun · Mikhail Yurochkin
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient
David Applegate · Mateo Diaz · Oliver Hinder · Haihao Lu · Miles Lubin · Brendan O'Donoghue · Warren Schudy
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning
Neehar Peri · Michael Curry · Samuel Dooley · John Dickerson
Private and Non-private Uniformity Testing for Ranking Data
Róbert Busa-Fekete · Dimitris Fotakis · Emmanouil Zampetakis
Privately Learning Mixtures of Axis-Aligned Gaussians
Ishaq Aden-Ali · Hassan Ashtiani · Christopher Liaw
Privately Learning Subspaces
Vikrant Singhal · Thomas Steinke
Privately Publishable Per-instance Privacy
Rachel Redberg · Yu-Xiang Wang
Probabilistic Forecasting: A Level-Set Approach
Hilaf Hasson · Bernie Wang · Tim Januschowski · Jan Gasthaus
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning
Andrea Zanette · Martin J Wainwright · Emma Brunskill
Provable Representation Learning for Imitation with Contrastive Fourier Features
Ofir Nachum · Mengjiao (Sherry) Yang
Reinforcement Learning Enhanced Explainer for Graph Neural Networks
Caihua Shan · Yifei Shen · Yao Zhang · Xiang Li · Dongsheng Li
Reinforcement learning for optimization of variational quantum circuit architectures
Mateusz Ostaszewski · Lea M. Trenkwalder · Wojciech Masarczyk · Eleanor Scerri · Vedran Dunjko
Relaxed Marginal Consistency for Differentially Private Query Answering
Ryan McKenna · Siddhant Pradhan · Daniel Sheldon · Gerome Miklau
Reliable Decisions with Threshold Calibration
Roshni Sahoo · Shengjia Zhao · Alyssa Chen · Stefano Ermon
Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning
Antonious Girgis · Deepesh Data · Suhas Diggavi
Replay-Guided Adversarial Environment Design
Minqi Jiang · Michael Dennis · Jack Parker-Holder · Jakob Foerster · Edward Grefenstette · Tim Rocktäschel
Representation Costs of Linear Neural Networks: Analysis and Design
Zhen Dai · Mina Karzand · Nathan Srebro
Representation Learning Beyond Linear Prediction Functions
Ziping Xu · Ambuj Tewari
Representation Learning for Event-based Visuomotor Policies
Sai Vemprala · Sami Mian · Ashish Kapoor
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
Jimmy Smith · Scott Linderman · David Sussillo
Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations
Wouter Van Gansbeke · Simon Vandenhende · Stamatios Georgoulis · Luc V Gool
Risk-Averse Bayes-Adaptive Reinforcement Learning
Marc Rigter · Bruno Lacerda · Nick Hawes
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures
Yuan Cao · Quanquan Gu · Mikhail Belkin
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning
Aurelien Bibaut · Nathan Kallus · Maria Dimakopoulou · Antoine Chambaz · Mark van der Laan
RL for Latent MDPs: Regret Guarantees and a Lower Bound
Jeongyeol Kwon · Yonathan Efroni · Constantine Caramanis · Shie Mannor
Robust Allocations with Diversity Constraints
Zeyu Shen · Lodewijk Gelauff · Ashish Goel · Aleksandra Korolova · Kamesh Munagala
Safe Reinforcement Learning with Natural Language Constraints
Tsung-Yen Yang · Michael Y Hu · Yinlam Chow · Peter J Ramadge · Karthik Narasimhan
Sampling with Trusthworthy Constraints: A Variational Gradient Framework
Xingchao Liu · Xin Tong · Qiang Liu
Scalable Online Planning via Reinforcement Learning Fine-Tuning
Arnaud Fickinger · Hengyuan Hu · Brandon Amos · Stuart Russell · Noam Brown
Scalars are universal: Equivariant machine learning, structured like classical physics
Soledad Villar · David W Hogg · Kate Storey-Fisher · Weichi Yao · Ben Blum-Smith
Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets
Max Ryabinin · Andrey Malinin · Mark Gales
Score-based Generative Modeling in Latent Space
Arash Vahdat · Karsten Kreis · Jan Kautz
Searching for Efficient Transformers for Language Modeling
David So · Wojciech Mańke · Hanxiao Liu · Zihang Dai · Noam Shazeer · Quoc V Le
Shaping embodied agent behavior with activity-context priors from egocentric video
Tushar Nagarajan · Kristen Grauman
Shared Independent Component Analysis for Multi-Subject Neuroimaging
Hugo Richard · Pierre Ablin · Bertrand Thirion · Alexandre Gramfort · Aapo Hyvarinen
Similarity and Matching of Neural Network Representations
Adrián Csiszárik · Péter Kőrösi-Szabó · Ákos Matszangosz · Gergely Papp · Dániel Varga
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
Koby Bibas · Meir Feder · Tal Hassner
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression
Steve Yadlowsky · Taedong Yun · Cory Y McLean · Alexander D'Amour
SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation
Abhinav Moudgil · Arjun Majumdar · Harsh Agrawal · Stefan Lee · Dhruv Batra
Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent
Emmanouil-Vasileios Vlatakis-Gkaragkounis · Lampros Flokas · Georgios Piliouras
Speech-T: Transducer for Text to Speech and Beyond
Jiawei Chen · Xu Tan · Yichong Leng · Jin Xu · Guihua Wen · Tao Qin · Tie-Yan Liu
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation
Nicklas Hansen · Hao Su · Xiaolong Wang
Statistical Query Lower Bounds for List-Decodable Linear Regression
Ilias Diakonikolas · Daniel Kane · Ankit Pensia · Thanasis Pittas · Alistair Stewart
Stochastic $L^\natural$-convex Function Minimization
Haixiang Zhang · Zeyu Zheng · Javad Lavaei
Stochastic Bias-Reduced Gradient Methods
Hilal Asi · Yair Carmon · Arun Jambulapati · Yujia Jin · Aaron Sidford
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity
Nicolas Loizou · Hugo Berard · Gauthier Gidel · Ioannis Mitliagkas · Simon Lacoste-Julien
Storchastic: A Framework for General Stochastic Automatic Differentiation
Emile van Krieken · Jakub Tomczak · Annette Ten Teije
Streaming Linear System Identification with Reverse Experience Replay
Suhas Kowshik · Dheeraj Nagaraj · Prateek Jain · Praneeth Netrapalli
SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning
Talip Ucar · Ehsan Hajiramezanali · Lindsay Edwards
Support vector machines and linear regression coincide with very high-dimensional features
Navid Ardeshir · Clayton Sanford · Daniel Hsu
Surrogate Regret Bounds for Polyhedral Losses
Rafael Frongillo · Bo Waggoner
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs
Taebum Kim · Eunji Jeong · Geon-Woo Kim · Yunmo Koo · Sehoon Kim · Gyeongin Yu · Byung-Gon Chun
Test-time Collective Prediction
Celestine Mendler-Dünner · Wenshuo Guo · Stephen Bates · Michael Jordan
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization
Daniel LeJeune · Hamid Javadi · Richard Baraniuk
The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains
Daron Anderson · Douglas Leith
The Skellam Mechanism for Differentially Private Federated Learning
Naman Agarwal · Peter Kairouz · Ken Liu
Towards Best-of-All-Worlds Online Learning with Feedback Graphs
Liad Erez · Tomer Koren
Towards Context-Agnostic Learning Using Synthetic Data
Charles Jin · Martin Rinard
Towards Multi-Grained Explainability for Graph Neural Networks
Xiang Wang · Yingxin Wu · An Zhang · Xiangnan He · Tat-Seng Chua
Towards optimally abstaining from prediction with OOD test examples
Adam Kalai · Varun Kanade
Towards robust vision by multi-task learning on monkey visual cortex
Shahd Safarani · Arne Nix · Konstantin Willeke · Santiago Cadena · Kelli Restivo · George Denfield · Andreas Tolias · Fabian Sinz
Towards Tight Communication Lower Bounds for Distributed Optimisation
Janne H. Korhonen · Dan Alistarh
Training Over-parameterized Models with Non-decomposable Objectives
Harikrishna Narasimhan · Aditya Menon
Tree in Tree: from Decision Trees to Decision Graphs
Bingzhao Zhu · Mahsa Shoaran
Twice regularized MDPs and the equivalence between robustness and regularization
Esther Derman · Matthieu Geist · Shie Mannor
Two-sided fairness in rankings via Lorenz dominance
Virginie Do · Sam Corbett-Davies · Jamal Atif · Nicolas Usunier
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization
Clement Gehring · Kenji Kawaguchi · Jiaoyang Huang · Leslie Kaelbling
Unsupervised Learning of Compositional Energy Concepts
Yilun Du · Shuang Li · Yash Sharma · Josh Tenenbaum · Igor Mordatch
Unsupervised Motion Representation Learning with Capsule Autoencoders
Ziwei Xu · Xudong Shen · Yongkang Wong · Mohan Kankanhalli
Unsupervised Speech Recognition
Alexei Baevski · Wei-Ning Hsu · Alexis CONNEAU · Michael Auli
User-Level Differentially Private Learning via Correlated Sampling
Badih Ghazi · Ravi Kumar · Pasin Manurangsi
ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction
Gengshan Yang · Deqing Sun · Varun Jampani · Daniel Vlasic · Forrester Cole · Ce Liu · Deva Ramanan
Who Leads and Who Follows in Strategic Classification?
Tijana Zrnic · Eric Mazumdar · Shankar Sastry · Michael Jordan
Go to Event Page
Demonstration

Demonstrations 3

Douwe Kiela · Barbara Caputo · Marco Ciccone
8:30 AM - 9:35 AM

Demonstrations must show novel technology and must run online during the conference. Unlike poster presentations or slide shows, interaction with the audience is a critical element. Therefore, the creativity of demonstrators to propose new ways in which interaction and engagement can fully leverage this year’s virtual conference format will be particularly relevant for selection. This session has the following demonstrations:

  • PYLON: A PyTorch Framework for Learning with Constraints
  • Real-Time and Accurate Self-Supervised Monocular Depth Estimation on Mobile Device
  • Unsupervised Indoor Wi-Fi Positioning
  • Prospective Explanations: An Interactive Mechanism for Model Understanding
... more
Datasets and Benchmarks

Dataset and Benchmark Poster Session 3

Joaquin Vanschoren · Serena Yeung
8:30 AM - 10:00 AM

The Datasets and Benchmarks track serves as a novel venue for high-quality publications, talks, and posters on highly valuable machine learning datasets and benchmarks, as well as a forum for discussions on how to improve dataset development. Datasets and benchmarks are crucial for the development of machine learning methods, but also require their own publishing and reviewing guidelines. For instance, datasets can often not be reviewed in a double-blind fashion, and hence full anonymization will not be required. On the other hand, they do require additional specific checks, such as a proper description of how the data was collected, whether they show intrinsic bias, and whether they will remain accessible.

... more
Competition

Competition Track Day 3: Overviews + Breakout Sessions

Douwe Kiela · Marco Ciccone · Barbara Caputo
10:00 AM - 3:04 PM

The program includes a wide variety of exciting competitions in different domains, with some focusing more on applications and others trying to unify fields, focusing on technical challenges or directly tackling important problems in the world. The aim is for the broad program to make it so that anyone who wants to work on or learn from a competition can find something to their liking.

In this session, we have the following competitions:
* Evaluating Approximate Inference in Bayesian Deep Learning
* The NetHack Challenge
* Machine Learning for Combinatorial Optimization
* Traffic4cast 2021 - Temporal and Spatial Few-Shot Transfer Learning in Traffic Map Movie Forecasting
* BASALT: A MineRL Competition on Solving Human-Judged Tasks
* IGLU: Interactive Grounded Language Understanding in a Collaborative Environment

... more
Panel
10:00 AM - 11:00 AM

Grappling with copyright law is unavoidable for ML researchers. Copyright protects works like text, photographs, and videos--all of which are used as ML training data, often without consent of the copyright owner. Relying on public domain works (like works published pre-1926), Creative Commons-licensed data (like Wikipedia) or ubiquitous data (like the Enron emails) seems like an easy way to avoid dealing with copyright. Unfortunately, only relying on those works predictably introduces bias into ML algorithms. This Workshop will not provide any legal advice, but it will equip researchers with the tools to understand copyright law and its relationship to ML bias, how the fair use doctrine may allow some copyrighted works to be used as training data without consent, and resources for obtaining legal advice related to copyright and ML research. Attendees will be able to participate in a Q&A after the presentation.

These are some of the resources mentioned in the discussion:

  • Friendly Neighborhood Tech Clinics (no single website, but offices are scattered throughout the US and possibly other countries)
  • How Copyright Law Can Fix Artificial Intelligence’s Implicit Bias Problem
  • Paper: Resisting Face Surveillance with Copyright Law
  • Paper: How Copyright Law Can Fix Artificial Intelligence's Implicit Bias Problem
  • Paper: Fair Learning by Mark Lemley + Bryan Casey
... more
Social
11:00 AM - 12:00 PM

Come learn and share best practices collaborating with researchers around the world, and discuss how to bridge the remote work, cultural, and social divides.

As Grid.ai and PyTorch Lightning are a global remote-first workplace, will be holding two special guest speakers from the company to discuss how they have improved their global research collaboration and communication. Q&A will follow, then we’ll break into networking for the last half of the hour. The whiteboards in our Gather space are meant for sharing collaboration ideas with others in the space.

... more
Social

Women in AI Ignite

Anoush Najarian
11:00 AM - 12:00 PM

Join us for 5-minute Ignite talks by women in AI. Excited for the Third Annual Women in AI Ignite at NeurIPS. The idea of Women in AI Ignite Social is based on our experience hosting Women in Tech Ignite sessions at conferences around the world. Everyone is welcome; our speakers are women.

... more
Datasets and Benchmarks

Dataset and Benchmark Symposium

Joaquin Vanschoren · Serena Yeung
11:00 AM - 2:00 PM

The Datasets and Benchmarks track serves as a novel venue for high-quality publications, talks, and posters on highly valuable machine learning datasets and benchmarks, as well as a forum for discussions on how to improve dataset development. Datasets and benchmarks are crucial for the development of machine learning methods, but also require their own publishing and reviewing guidelines. For instance, datasets can often not be reviewed in a double-blind fashion, and hence full anonymization will not be required. On the other hand, they do require additional specific checks, such as a proper description of how the data was collected, whether they show intrinsic bias, and whether they will remain accessible.

... more
Invited Talk
3:00 PM - 4:30 PM

In October 2021, X officially became an option for gender on US passports. What are the computational changes necessary to adapt to this more inclusive gender option? In this talk, Meredith Broussard investigates why large-scale computer systems are stuck using 1950s ideas about gender, and what is needed to update sociotechnical systems. She explores how allies can leverage public interest technology in order to think beyond the gender binary, interrogate and audit software systems, and create code for social good.

... more
Poster
4:30 PM - 6:00 PM
224 Events in this session
Adaptive wavelet distillation from neural networks through interpretations
Wooseok Ha · Chandan Singh · Francois Lanusse · Srigokul Upadhyayula · Bin Yu
Auditing Black-Box Prediction Models for Data Minimization Compliance
Bashir Rastegarpanah · Krishna Gummadi · Mark Crovella
CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings
Tatiana Likhomanenko · Qiantong Xu · Gabriel Synnaeve · Ronan Collobert · Alex Rogozhnikov
Conflict-Averse Gradient Descent for Multi-task learning
Bo Liu · Xingchao Liu · Xiaojie Jin · Peter Stone · Qiang Liu
Decoupling the Depth and Scope of Graph Neural Networks
Hanqing Zeng · Muhan Zhang · Yinglong Xia · Ajitesh Srivastava · Andrey Malevich · Rajgopal Kannan · Viktor Prasanna · Long Jin · Ren Chen
Differentially Private Federated Bayesian Optimization with Distributed Exploration
Zhongxiang Dai · Bryan Kian Hsiang Low · Patrick Jaillet
Dynamic Trace Estimation
Prathamesh Dharangutte · Christopher Musco
Episodic Multi-agent Reinforcement Learning with Curiosity-driven Exploration
Lulu Zheng · Jiarui Chen · Jianhao Wang · Jiamin He · Yujing Hu · Yingfeng Chen · Changjie Fan · Yang Gao · Chongjie Zhang
Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding
Yang Li · Si Si · Gang Li · Cho-Jui Hsieh · Samy Bengio
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update
Michal Derezinski · Jonathan Lacotte · Mert Pilanci · Michael Mahoney
On the Equivalence between Neural Network and Support Vector Machine
Yilan Chen · Wei Huang · Lam Nguyen · Tsui-Wei Weng
On the Generative Utility of Cyclic Conditionals
Chang Liu · Haoyue Tang · Tao Qin · Jintao Wang · Tie-Yan Liu
Optimal Underdamped Langevin MCMC Method
Zhengmian Hu · Feihu Huang · Heng Huang
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem
Eric Liang · Zhanghao Wu · Michael Luo · Sven Mika · Joseph Gonzalez · Ion Stoica
Robust Visual Reasoning via Language Guided Neural Module Networks
Arjun Akula · Varun Jampani · Soravit Changpinyo · Song-Chun Zhu
Safe Policy Optimization with Local Generalized Linear Function Approximations
Akifumi Wachi · Yunyue Wei · Yanan Sui
Stochastic optimization under time drift: iterate averaging, step-decay schedules, and high probability guarantees
Joshua Cutler · Dmitriy Drusvyatskiy · Zaid Harchaoui
Stronger NAS with Weaker Predictors
Junru Wu · Xiyang Dai · Dongdong Chen · Yinpeng Chen · Mengchen Liu · Ye Yu · Zhangyang Wang · Zicheng Liu · Mei Chen · Lu Yuan
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning
Christopher Hoang · Sungryull Sohn · Jongwook Choi · Wilka Carvalho · Honglak Lee
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients
Feihu Huang · Junyi Li · Heng Huang
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Mucong Ding · Kezhi Kong · Jingling Li · Chen Zhu · John Dickerson · Furong Huang · Tom Goldstein
A Biased Graph Neural Network Sampler with Near-Optimal Regret
Qingru Zhang · David Wipf · Quan Gan · Le Song
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Runzhong Wang · Zhigang Hua · Gan Liu · Jiayi Zhang · Junchi Yan · Feng Qi · Shuang Yang · Jun Zhou · Xiaokang Yang
A Computationally Efficient Method for Learning Exponential Family Distributions
Abhin Shah · Devavrat Shah · Gregory Wornell
A Contrastive Learning Approach for Training Variational Autoencoder Priors
Jyoti Aneja · Alex Schwing · Jan Kautz · Arash Vahdat
Adaptive Diffusion in Graph Neural Networks
Jialin Zhao · Yuxiao Dong · Ming Ding · Evgeny Kharlamov · Jie Tang
A Minimalist Approach to Offline Reinforcement Learning
Scott Fujimoto · Shixiang (Shane) Gu
An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks
Shashank Rajput · Kartik Sreenivasan · Dimitris Papailiopoulos · Amin Karbasi
An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap
Yuanhao Wang · Ruosong Wang · Sham Kakade
Approximate optimization of convex functions with outlier noise
Anindya De · Sanjeev Khanna · Huan Li · MohammadHesam NikpeySalekde
A Regression Approach to Learning-Augmented Online Algorithms
Keerti Anand · Rong Ge · Amit Kumar · Debmalya Panigrahi
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis
Xingang Pan · Xudong XU · Chen Change Loy · Christian Theobalt · Bo Dai
Associative Memories via Predictive Coding
Tommaso Salvatori · Yuhang Song · Yujian Hong · Lei Sha · Simon Frieder · Zhenghua Xu · Rafal Bogacz · Thomas Lukasiewicz
Asymptotically Exact Error Characterization of Offline Policy Evaluation with Misspecified Linear Models
Kohei Miyaguchi
A Unified View of cGANs with and without Classifiers
Si-An Chen · Chun-Liang Li · Hsuan-Tien Lin
AutoBalance: Optimized Loss Functions for Imbalanced Data
Mingchen Li · Xuechen Zhang · Christos Thrampoulidis · Jiasi Chen · Samet Oymak
Automated Dynamic Mechanism Design
Hanrui Zhang · Vincent Conitzer
Backdoor Attack with Imperceptible Input and Latent Modification
Khoa Doan · Yingjie Lao · Ping Li
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer
Haoping Bai · Meng Cao · Ping Huang · Jiulong Shan
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
Yiqin Yang · Xiaoteng Ma · Chenghao Li · Zewu Zheng · Qiyuan Zhang · Gao Huang · Jun Yang · Qianchuan Zhao
Bounds all around: training energy-based models with bidirectional bounds
Cong Geng · Jia Wang · Zhiyong Gao · Jes Frellsen · Søren Hauberg
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
Will Stephenson · Zachary Frangella · Madeleine Udell · Tamara Broderick
Causal Abstractions of Neural Networks
Atticus Geiger · Hanson Lu · Thomas Icard · Christopher Potts
Clockwork Variational Autoencoders
Vaibhav Saxena · Jimmy Ba · Danijar Hafner
Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems
Tianyi Chen · Yuejiao Sun · Wotao Yin
CoAtNet: Marrying Convolution and Attention for All Data Sizes
Zihang Dai · Hanxiao Liu · Quoc V Le · Mingxing Tan
Combiner: Full Attention Transformer with Sparse Computation Cost
Hongyu Ren · Hanjun Dai · Zihang Dai · Mengjiao (Sherry) Yang · Jure Leskovec · Dale Schuurmans · Bo Dai
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers
Albert Gu · Isys Johnson · Karan Goel · Khaled Saab · Tri Dao · Atri Rudra · Christopher Ré
COMBO: Conservative Offline Model-Based Policy Optimization
Tianhe Yu · Aviral Kumar · Rafael Rafailov · Aravind Rajeswaran · Sergey Levine · Chelsea Finn
Conservative Offline Distributional Reinforcement Learning
Jason Yecheng Ma · Dinesh Jayaraman · Osbert Bastani
Constrained Robust Submodular Partitioning
Shengjie Wang · Tianyi Zhou · Chandrashekhar Lavania · Jeff A Bilmes
Continuous Latent Process Flows
Ruizhi Deng · Marcus Brubaker · Greg Mori · Andreas Lehrmann
Continuous-time edge modelling using non-parametric point processes
Xuhui Fan · Bin Li · Feng Zhou · Scott SIsson
Contrastively Disentangled Sequential Variational Autoencoder
Junwen Bai · Weiran Wang · Carla Gomes
Controlling Neural Networks with Rule Representations
Sungyong Seo · Sercan Arik · Jinsung Yoon · Xiang Zhang · Kihyuk Sohn · Tomas Pfister
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights
Ganlin Song · Ruitu Xu · John Lafferty
Dataset Distillation with Infinitely Wide Convolutional Networks
Timothy Nguyen · Roman Novak · Lechao Xiao · Jaehoon Lee
Data Sharing and Compression for Cooperative Networked Control
Jiangnan Cheng · Marco Pavone · Sachin Katti · Sandeep Chinchali · Ao Tang
Deconvolutional Networks on Graph Data
Jia Li · Jiajin Li · Yang Liu · Jianwei Yu · Yueting Li · Hong Cheng
Deep Extrapolation for Attribute-Enhanced Generation
Alvin Chan · Ali Madani · Ben Krause · Nikhil Naik
Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks
Woochul Kang · Daeyeon Kim
Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess
Gregory Clark
DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
Wenzheng Chen · Joey Litalien · Jun Gao · Zian Wang · Clement Fuji Tsang · Sameh Khamis · Or Litany · Sanja Fidler
Differentially Private Learning with Adaptive Clipping
Galen Andrew · Om Thakkar · Brendan McMahan · Swaroop Ramaswamy
Differentially Private Model Personalization
Prateek Jain · John Rush · Adam Smith · Shuang Song · Abhradeep Guha Thakurta
Differential Privacy Over Riemannian Manifolds
Matthew Reimherr · Karthik Bharath · Carlos Soto
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
Valentin De Bortoli · James Thornton · Jeremy Heng · Arnaud Doucet
Dimension-free empirical entropy estimation
Doron Cohen · Aryeh Kontorovich · Aaron Koolyk · Geoffrey Wolfer
Discovering and Achieving Goals via World Models
Russell Mendonca · Oleh Rybkin · Kostas Daniilidis · Danijar Hafner · Deepak Pathak
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Yifei Wang · Yisen Wang · Jiansheng Yang · Zhouchen Lin
Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition
Jayadev Acharya · Clement Canonne · Yuhan Liu · Ziteng Sun · Himanshu Tyagi
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals
Lang Liu · Krishna Pillutla · Sean Welleck · Sewoong Oh · Yejin Choi · Zaid Harchaoui
DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks
Wei Sun · Aojun Zhou · Sander Stuijk · Rob Wijnhoven · Andrew Nelson · Hongsheng Li · Henk Corporaal
Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model
Yuan Chen · Wenbo Fei · Qinxia Wang · Donglin Zeng · Yuanjia Wang
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data
Ashraful Islam · Chun-Fu (Richard) Chen · Rameswar Panda · Leonid Karlinsky · Rogerio Feris · Richard J. Radke
Dynamic Inference with Neural Interpreters
Nasim Rahaman · Muhammad Waleed Gondal · Shruti Joshi · Peter Gehler · Yoshua Bengio · Francesco Locatello · Bernhard Schölkopf
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language
Mingyu Ding · Zhenfang Chen · Tao Du · Ping Luo · Josh Tenenbaum · Chuang Gan
Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations
Pranjal Awasthi · Alex Tang · Aravindan Vijayaraghavan
Efficiently Identifying Task Groupings for Multi-Task Learning
Chris Fifty · Ehsan Amid · Zhe Zhao · Tianhe Yu · Rohan Anil · Chelsea Finn
Efficiently Learning One Hidden Layer ReLU Networks From Queries
Sitan Chen · Adam Klivans · Raghu Meka
Efficient Neural Network Training via Forward and Backward Propagation Sparsification
Xiao Zhou · Weizhong Zhang · Zonghao Chen · SHIZHE DIAO · Tong Zhang
Efficient Statistical Assessment of Neural Network Corruption Robustness
Karim TIT · Teddy Furon · Mathias ROUSSET
Embedding Principle of Loss Landscape of Deep Neural Networks
Yaoyu Zhang · Zhongwang Zhang · Tao Luo · Zhiqin J Xu
Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots
Jagdeep Bhatia · Holly Jackson · Yunsheng Tian · Jie Xu · Wojciech Matusik
Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions
Huangjie Zheng · Mingyuan Zhou
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning
Yingjie Fei · Zhuoran Yang · Yudong Chen · Zhaoran Wang
Extracting Deformation-Aware Local Features by Learning to Deform
Guilherme Potje · Renato Martins · Felipe Chamone · Erickson Nascimento
Fairness in Ranking under Uncertainty
Ashudeep Singh · David Kempe · Thorsten Joachims
Fairness via Representation Neutralization
Mengnan Du · Subhabrata Mukherjee · Guanchu Wang · Ruixiang Tang · Ahmed Awadallah · Xia Hu
Fair Sequential Selection Using Supervised Learning Models
Mahdi Khalili · Xueru Zhang · Mahed Abroshan
Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation
Hideaki Kim
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds
Shahrzad Haddadan · Yue Zhuang · Cyrus Cousins · Eli Upfal
Fast Extra Gradient Methods for Smooth Structured Nonconvex-Nonconcave Minimax Problems
Sucheol Lee · Donghwan Kim
Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization
Shicong Cen · Yuting Wei · Yuejie Chi
Fast Training Method for Stochastic Compositional Optimization Problems
Hongchang Gao · Heng Huang
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
Quoc Tran Dinh · Nhan H Pham · Dzung Phan · Lam Nguyen
Few-Shot Segmentation via Cycle-Consistent Transformer
Gengwei Zhang · Guoliang Kang · Yi Yang · Yunchao Wei
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators
Zaiwei Chen · Siva Theja Maguluri · Sanjay Shakkottai · Karthikeyan Shanmugam
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery
Huaxiu Yao · Ying Wei · Long-Kai Huang · Ding Xue · Junzhou Huang · Zhenhui (Jessie) Li
Functional Regularization for Reinforcement Learning via Learned Fourier Features
Alex Li · Deepak Pathak
Generalized Linear Bandits with Local Differential Privacy
Yuxuan Han · Zhipeng Liang · Yang Wang · Jiheng Zhang
Generalized Shape Metrics on Neural Representations
Alex H Williams · Erin Kunz · Simon Kornblith · Scott Linderman
Geometry Processing with Neural Fields
Guandao Yang · Serge Belongie · Bharath Hariharan · Vladlen Koltun
Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games
Tanner Fiez · Lillian Ratliff · Eric Mazumdar · Evan Faulkner · Adhyyan Narang
Grounding Representation Similarity Through Statistical Testing
Frances Ding · Jean-Stanislas Denain · Jacob Steinhardt
Heuristic-Guided Reinforcement Learning
Ching-An Cheng · Andrey Kolobov · Adith Swaminathan
High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails
Ashok Cutkosky · Harsh Mehta
How can classical multidimensional scaling go wrong?
Rishi Sonthalia · Greg Van Buskirk · Benjamin Raichel · Anna Gilbert
HyperSPNs: Compact and Expressive Probabilistic Circuits
Andy Shih · Dorsa Sadigh · Stefano Ermon
Identity testing for Mallows model
Róbert Busa-Fekete · Dimitris Fotakis · Balazs Szorenyi · Emmanouil Zampetakis
Imitation with Neural Density Models
Kuno Kim · Akshat Jindal · Yang Song · Jiaming Song · Yanan Sui · Stefano Ermon
Implicit Transformer Network for Screen Content Image Continuous Super-Resolution
Jingyu Yang · Sheng Shen · Huanjing Yue · Kun Li
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning
Maxwell Nye · Michael Tessler · Josh Tenenbaum · Brenden Lake
Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration
Yu Wang · Jingyang Lin · Jingjing Zou · Yingwei Pan · Ting Yao · Tao Mei
Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity
Vladimir Ivanov · Konstantinos Michmizos
Information-constrained optimization: can adaptive processing of gradients help?
Jayadev Acharya · Clement Canonne · Prathamesh Mayekar · Himanshu Tyagi
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees
Gregory Dexter · Kevin Bello · Jean Honorio
Joint inference and input optimization in equilibrium networks
Swaminathan Gurumurthy · Shaojie Bai · Zachary Manchester · J. Zico Kolter
Knowledge-inspired 3D Scene Graph Prediction in Point Cloud
Shoulong Zhang · shuai li · Aimin Hao · Hong Qin
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Derek Lim · Felix Hohne · Xiuyu Li · Sijia Linda Huang · Vaishnavi Gupta · Omkar Bhalerao · Ser Nam Lim
Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages
Xinyun Chen · Dawn Song · Yuandong Tian
Learning 3D Dense Correspondence via Canonical Point Autoencoder
An-Chieh Cheng · Xueting Li · Min Sun · Ming-Hsuan Yang · Sifei Liu
Learning Domain Invariant Representations in Goal-conditioned Block MDPs
Beining Han · Chongyi Zheng · Harris Chan · Keiran Paster · Michael Zhang · Jimmy Ba
Learning in Multi-Stage Decentralized Matching Markets
Xiaowu Dai · Michael Jordan
Learning Optimal Predictive Checklists
Haoran Zhang · Quaid Morris · Berk Ustun · Marzyeh Ghassemi
Learning Signal-Agnostic Manifolds of Neural Fields
Yilun Du · Katie Collins · Josh Tenenbaum · Vincent Sitzmann
Learning to Assimilate in Chaotic Dynamical Systems
John McCabe · Jed Brown
Learning to delegate for large-scale vehicle routing
Sirui Li · Zhongxia Yan · Cathy Wu
Learning to Learn Dense Gaussian Processes for Few-Shot Learning
Ze Wang · Zichen Miao · Xiantong Zhen · Qiang Qiu
Learning to Select Exogenous Events for Marked Temporal Point Process
Ping Zhang · Rishabh Iyer · Ashish Tendulkar · Gaurav Aggarwal · Abir De
Locality Sensitive Teaching
Zhaozhuo Xu · Beidi Chen · Chaojian Li · Weiyang Liu · Le Song · Yingyan Lin · Anshumali Shrivastava
Long Short-Term Transformer for Online Action Detection
Mingze Xu · Yuanjun Xiong · Hao Chen · Xinyu Li · Wei Xia · Zhuowen Tu · Stefano Soatto
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning
FEIHU ZHANG · Philip Torr · Rene Ranftl · Stephan Richter
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions
Yin Tat Lee · Ruoqi Shen · Kevin Tian
Matrix encoding networks for neural combinatorial optimization
Yeong-Dae Kwon · Jinho Choo · Iljoo Yoon · Minah Park · Duwon Park · Youngjune Gwon
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination
Arpan Mukherjee · Ali Tajer · Pin-Yu Chen · Payel Das
Meta-Adaptive Nonlinear Control: Theory and Algorithms
Guanya Shi · Kamyar Azizzadenesheli · Michael O'Connell · Soon-Jo Chung · Yisong Yue
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models
Runzhe Wan · Lin Ge · Rui Song
Meta-learning to Improve Pre-training
Aniruddh Raghu · Jonathan Lorraine · Simon Kornblith · Matthew McDermott · David Duvenaud
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization
Qi Deng · Wenzhi Gao
Model-Based Episodic Memory Induces Dynamic Hybrid Controls
Hung Le · Thommen Karimpanal George · Majid Abdolshah · Truyen Tran · Svetha Venkatesh
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Yushi Bai · Zhitao Ying · Hongyu Ren · Jure Leskovec
Momentum Centering and Asynchronous Update for Adaptive Gradient Methods
Juntang Zhuang · Yifan Ding · Tommy Tang · Nicha Dvornek · Sekhar C Tatikonda · James Duncan
Multi-Agent Reinforcement Learning in Stochastic Networked Systems
Yiheng Lin · Guannan Qu · Longbo Huang · Adam Wierman
Multiwavelet-based Operator Learning for Differential Equations
Gaurav Gupta · Xiongye Xiao · Paul Bogdan
Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks
Hidenori Tanaka · Daniel Kunin
Observation-Free Attacks on Stochastic Bandits
Yinglun Xu · Bhuvesh Kumar · Jacob Abernethy
Online Robust Reinforcement Learning with Model Uncertainty
Yue Wang · Shaofeng Zou
Online Selective Classification with Limited Feedback
Aditya Gangrade · Anil Kag · Ashok Cutkosky · Venkatesh Saligrama
On Path Integration of Grid Cells: Group Representation and Isotropic Scaling
Ruiqi Gao · Jianwen Xie · Xue-Xin Wei · Song-Chun Zhu · Ying Nian Wu
On the Representation of Solutions to Elliptic PDEs in Barron Spaces
Ziang Chen · Jianfeng Lu · Yulong Lu
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
Robin Winter · Frank Noe · Djork-Arné Clevert
Perturbation Theory for the Information Bottleneck
Vudtiwat Ngampruetikorn · David Schwab
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL
Minshuo Chen · Yan Li · Ethan Wang · Zhuoran Yang · Zhaoran Wang · Tuo Zhao
PLUGIn: A simple algorithm for inverting generative models with recovery guarantees
Babhru Joshi · Xiaowei Li · Yaniv Plan · Ozgur Yilmaz
Pointwise Bounds for Distribution Estimation under Communication Constraints
Wei-Ning Chen · Peter Kairouz · Ayfer Ozgur
Probabilistic Transformer For Time Series Analysis
Binh Tang · David S Matteson
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction
Yaqing Wang · Abulikemu Abuduweili · Quanming Yao · Dejing Dou
ProTo: Program-Guided Transformer for Program-Guided Tasks
Zelin Zhao · Karan Samel · Binghong Chen · lee song
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent
Spencer Frei · Quanquan Gu
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes
Sanghyun Hong · Michael-Andrei Panaitescu-Liess · Yigitcan Kaya · Tudor Dumitras
Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections
NABARUN DEB · Promit Ghosal · Bodhisattva Sen
Realistic evaluation of transductive few-shot learning
Olivier Veilleux · Malik Boudiaf · Pablo Piantanida · Ismail Ben Ayed
RED : Looking for Redundancies for Data-FreeStructured Compression of Deep Neural Networks
Edouard YVINEC · Arnaud Dapogny · Matthieu Cord · Kevin Bailly
Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks
Chenning Yu · Sicun Gao
Regulating algorithmic filtering on social media
Sarah Cen · Devavrat Shah
Reinforcement Learning based Disease Progression Model for Alzheimer’s Disease
Krishnakant Saboo · Anirudh Choudhary · Yurui Cao · Gregory Worrell · David Jones · Ravishankar Iyer
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
Dylan Slack · Anna Hilgard · Sameer Singh · Himabindu Lakkaraju
ReLU Regression with Massart Noise
Ilias Diakonikolas · Jong Ho Park · Christos Tzamos
REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision
Mihai Fieraru · Mihai Zanfir · Teodor Szente · Eduard Bazavan · Vlad Olaru · Cristian Sminchisescu
Representing Hyperbolic Space Accurately using Multi-Component Floats
Tao Yu · Christopher De Sa
Revealing and Protecting Labels in Distributed Training
Trung Dang · Om Thakkar · Swaroop Ramaswamy · Rajiv Mathews · Peter Chin · Françoise Beaufays
Revisiting Smoothed Online Learning
Lijun Zhang · Wei Jiang · Shiyin Lu · Tianbao Yang
Risk Bounds and Calibration for a Smart Predict-then-Optimize Method
Heyuan Liu · Paul Grigas
Robust and differentially private mean estimation
Xiyang Liu · Weihao Kong · Sham Kakade · Sewoong Oh
Robust Counterfactual Explanations on Graph Neural Networks
Mohit Bajaj · Lingyang Chu · Zi Yu Xue · Jian Pei · Lanjun Wang · Peter Cho-Ho Lam · Yong Zhang
Robust Deep Reinforcement Learning through Adversarial Loss
Tuomas Oikarinen · Wang Zhang · Alexandre Megretski · Luca Daniel · Tsui-Wei Weng
Robust Learning of Optimal Auctions
Wenshuo Guo · Michael Jordan · Emmanouil Zampetakis
Robust Predictable Control
Ben Eysenbach · Russ Salakhutdinov · Sergey Levine
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding
Tengwei Song · Jie Luo · Lei Huang
SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning
Aaron Chan · Jiashu Xu · Boyuan Long · Soumya Sanyal · Tanishq Gupta · Xiang Ren
Sample Selection for Fair and Robust Training
Yuji Roh · Kangwook Lee · Steven Whang · Changho Suh
Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints
Henry C Bendekgey · Erik Sudderth
Self-Adaptable Point Processes with Nonparametric Time Decays
Zhimeng Pan · Zheng Wang · Jeff M Phillips · Shandian Zhe
SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement
Heyang Qin · Samyam Rajbhandari · Olatunji Ruwase · Feng Yan · Lei Yang · Yuxiong He
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning
ZHENHUAN YANG · Yunwen Lei · Puyu Wang · Tianbao Yang · Yiming Ying
Slice Sampling Reparameterization Gradients
David Zoltowski · Diana Cai · Ryan Adams
SOFT: Softmax-free Transformer with Linear Complexity
Jiachen Lu · Jinghan Yao · Junge Zhang · Xiatian Zhu · Hang Xu · Weiguo Gao · Chunjing XU · Tao Xiang · Li Zhang
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network
Xiaolin Hu · Kai Li · Weiyi Zhang · Yi Luo · Jean-Marie Lemercier · Timo Gerkmann
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
Fan Bao · Guoqiang Wu · Chongxuan LI · Jun Zhu · Bo Zhang
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
Prashant Khanduri · PRANAY SHARMA · Haibo Yang · Mingyi Hong · Jia Liu · Ketan Rajawat · Pramod Varshney
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training
Anup Sarma · Sonali Singh · Huaipan Jiang · Rui Zhang · Mahmut Kandemir · Chita Das
Subgame solving without common knowledge
Brian Zhang · Tuomas Sandholm
Subgraph Federated Learning with Missing Neighbor Generation
Ke ZHANG · Carl Yang · Xiaoxiao Li · Lichao Sun · Siu Ming Yiu
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer
Junya Chen · Zidi Xiu · Benjamin Goldstein · Ricardo Henao · Lawrence Carin · Chenyang Tao
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory
Takashi Matsubara · Yuto Miyatake · Takaharu Yaguchi
Systematic Generalization with Edge Transformers
Leon Bergen · Timothy O'Donnell · Dzmitry Bahdanau
Techniques for Symbol Grounding with SATNet
Sever Topan · David Rolnick · Xujie Si
The Benefits of Implicit Regularization from SGD in Least Squares Problems
Difan Zou · Jingfeng Wu · Vladimir Braverman · Quanquan Gu · Dean Foster · Sham Kakade
The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition
Tiancheng Jin · Longbo Huang · Haipeng Luo
The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization
Mufan Li · Mihai Nica · Dan Roy
Time-series Generation by Contrastive Imitation
Daniel Jarrett · Ioana Bica · Mihaela van der Schaar
Topographic VAEs learn Equivariant Capsules
T. Anderson Keller · Max Welling
TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis
Benjamin Attal · Eliot Laidlaw · Aaron Gokaslan · Changil Kim · Christian Richardt · James Tompkin · Matthew O'Toole
Towards understanding retrosynthesis by energy-based models
Ruoxi Sun · Hanjun Dai · Li Li · Steven Kearnes · Bo Dai
TriBERT: Human-centric Audio-visual Representation Learning
Tanzila Rahman · Mengyu Yang · Leonid Sigal
True Few-Shot Learning with Language Models
Ethan Perez · Douwe Kiela · Kyunghyun Cho
UCB-based Algorithms for Multinomial Logistic Regression Bandits
Sanae Amani · Christos Thrampoulidis
Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning
Youngkyu Hong · Eunho Yang
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
Gaon An · Seungyong Moon · Jang-Hyun Kim · Hyun Oh Song
Understanding and Improving Early Stopping for Learning with Noisy Labels
Yingbin Bai · Erkun Yang · Bo Han · Yanhua Yang · Jiatong Li · Yinian Mao · Gang Niu · Tongliang Liu
Understanding Deflation Process in Over-parametrized Tensor Decomposition
Rong Ge · Yunwei Ren · Xiang Wang · Mo Zhou
Understanding Instance-based Interpretability of Variational Auto-Encoders
Zhifeng Kong · Kamalika Chaudhuri
Understanding Interlocking Dynamics of Cooperative Rationalization
Mo Yu · Yang Zhang · Shiyu Chang · Tommi Jaakkola
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting
Frederic Koehler · Lijia Zhou · Danica J. Sutherland · Nathan Srebro
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation
Jiafan He · Dongruo Zhou · Quanquan Gu
Uniform Sampling over Episode Difficulty
Sébastien Arnold · Guneet Dhillon · Avinash Ravichandran · Stefano Soatto
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization
Deeksha Adil · Brian Bullins · Sushant Sachdeva
Unintended Selection: Persistent Qualification Rate Disparities and Interventions
Reilly Raab · Yang Liu
Universal Graph Convolutional Networks
Di Jin · Zhizhi Yu · Cuiying Huo · Rui Wang · Xiao Wang · Dongxiao He · Jiawei Han
VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media
Yizhou Zhang · Karishma Sharma · Yan Liu
What training reveals about neural network complexity
Andreas Loukas · Marinos Poiitis · Stefanie Jegelka
Which Mutual-Information Representation Learning Objectives are Sufficient for Control?
Kate Rakelly · Abhishek Gupta · Carlos Florensa · Sergey Levine
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks
Shuai Zhang · Meng Wang · Sijia Liu · Pin-Yu Chen · Jinjun Xiong
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Affinity Workshop

WiML Workshop 2

Soomin Aga Lee · Meera Desai · Nezihe Merve Gürel · Boyi Li · Linh Tran · Akiko Eriguchi · Jieyu Zhao · Salomey Osei · Sirisha Rambhatla · Geeticka Chauhan · Nwamaka (Amaka) Okafor · Mariya Vasileva
6:00 PM - 11:00 PM

WiML’s purpose is to enhance the experience of women in machine learning. Our flagship event is the annual Women in Machine Learning (WiML) Workshop, typically co-located with NeurIPS. We also organize an “un-workshop” at ICML, as well as small events at other machine learning conferences such as AISTATS, ICLR, etc.

Our mission is to enhance the experience of women in machine learning, and thereby

Increase the number of women in machine learning
Help women in machine learning succeed professionally
Increase the impact of women in machine learning in the community

Toward this goal, we create opportunities for women to engage in substantive technical and professional conversations in a positive, supportive environment (e.g. annual workshop, small events, mentoring program). We also work to increase awareness and appreciation of the achievements of women in machine learning (e.g. directory and profiles of women in machine learning). Our programs help women build their technical confidence and their voice, and our publicity efforts help ensure that women in machine learning and their achievements are known in the community.

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Town Hall
11:00 PM - 12:00 AM

The Town Hall meeting is an opportunity to connect and to learn about how this year's conference was organized. It is also a place where you can ask questions and provide feedback to the NeurIPS organizers and Board. We encourage you to submit questions in advance via townhall@neurips.cc. This event is open to all registered attendees of NeurIPS 2021.

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