NeurIPS 2022 Thursday 12/1

Timezone: America/Chicago

Full Schedule Mon 11/28 Tue 11/29 Wed 11/30 Thu 12/1 Fri 12/2 Sat 12/3 Sun 12/4 Mon 12/5 Tue 12/6 Wed 12/7 Thu 12/8 Fri 12/9

Registration Desk

8:00 AM - 6:00 PM

Invited Talk

The Data-Centric Era: How ML is Becoming an Experimental Science

Isabelle Guyon

9:30 AM - 10:30 AM

NeurIPS has been in existence for more than 3 decades, each one marked by a dominant trend. The pioneering years saw the burgeoning of back-prop nets, the coming-of-age years blossomed with convex optimization, regularization, Bayesian methods, boosting, kernel methods, to name a few, and the junior years have been dominated by deep nets and big data. And now, recent analyses conclude that using ever bigger data and deeper networks is not a sustainable way of progressing. Meanwhile, other indicators show that Machine Learning is increasingly reliant upon good data and benchmarks, not only to train more powerful and/or more compact models, but also to soundly evaluate new ideas and to stress test models on their reliability, fairness, and protection against various attacks, including privacy attacks.

Simultaneously, in 2021, the NeurIPS Dataset and Benchmark track was launched and the Data-Centric AI initiative was born. This kickstarted the "data-centric era". It is gaining momentum in response to the new needs of data scientists who, admittedly, spend more time on understanding problems, designing experimental settings, and engineering datasets, than on designing and training ML models.

We will retrace the enormous collective efforts made by our community since the 1980's to share datasets and benchmarks, putting forward important milestones that led us to today's effervescence. We will pick a few hot topics that have raised controversy and have engendered novel thought-provoking contributions. Finally, we will highlight some of the most pressing issues that must be addressed by the community.

... more

Speaker Bio

Isabelle Guyon recently joined Google Brain as a research scientist. She is also professor of artificial intelligence at Université Paris-Saclay (Orsay). Her areas of expertise include computer vision, bioinformatics, and power systems. She is best known for being a co-inventor of Support Vector Machines. Her recent interests are in automated machine learning, meta-learning, and data-centric AI. She has been a strong promoter of challenges and benchmarks, and is president of ChaLearn, a non-profit dedicated to organizing machine learning challenges. She is community lead of Codalab competitions, a challenge platform used both in academia and industry. She co-organized the “Challenges in Machine Learning Workshop” @ NeurIPS between 2014 and 2019, launched the "NeurIPS challenge track" in 2017 while she was general chair, and pushed the creation of the "NeurIPS datasets and benchmark track" in 2021, as a NeurIPS board member.

... more

Poster

Poster Session 5

11:00 AM - 1:00 PM

412 Events in this session

Learning Operators with Coupled Attention

Georgios Kissas · Jacob Seidman · Leonardo Ferreira Guilhoto · Victor M. Preciado · George J. Pappas · Paris Perdikaris

Single Model Uncertainty Estimation via Stochastic Data Centering

Jayaraman Thiagarajan · Rushil Anirudh · Vivek Sivaraman Narayanaswamy · Timo Bremer

A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity

Seo Taek Kong · Soomin Jeon · Dongbin Na · Jaewon Lee · Hong-Seok Lee · Kyu-Hwan Jung

Retaining Knowledge for Learning with Dynamic Definition

Zichang Liu · Benjamin Coleman · Tianyi Zhang · Anshumali Shrivastava

Amortized Inference for Causal Structure Learning

Lars Lorch · Scott Sussex · Jonas Rothfuss · Andreas Krause · Bernhard Schölkopf

Multi-Game Decision Transformers

Kuang-Huei Lee · Ofir Nachum · Mengjiao (Sherry) Yang · Lisa Lee · Daniel Freeman · Sergio Guadarrama · Ian Fischer · Winnie Xu · Eric Jang · Henryk Michalewski · Igor Mordatch

NS3: Neuro-symbolic Semantic Code Search

Shushan Arakelyan · Anna Hakhverdyan · Miltiadis Allamanis · Luis Garcia · Christophe Hauser · Xiang Ren

NeMF: Neural Motion Fields for Kinematic Animation

Chengan He · Jun Saito · James Zachary · Holly Rushmeier · Yi Zhou

COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics

Lianhui Qin · Sean Welleck · Daniel Khashabi · Yejin Choi

Towards Safe Reinforcement Learning with a Safety Editor Policy

Haonan Yu · Wei Xu · Haichao Zhang

A Solver-free Framework for Scalable Learning in Neural ILP Architectures

Yatin Nandwani · Rishabh Ranjan · - Mausam · Parag Singla

Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers

Wanqian Yang · Polina Kirichenko · Micah Goldblum · Andrew Wilson

Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation

Yang Ni

Concrete Score Matching: Generalized Score Matching for Discrete Data

Chenlin Meng · Kristy Choi · Jiaming Song · Stefano Ermon

Structuring Uncertainty for Fine-Grained Sampling in Stochastic Segmentation Networks

Frank Nussbaum · Jakob Gawlikowski · Julia Niebling

NOMAD: Nonlinear Manifold Decoders for Operator Learning

Jacob Seidman · Georgios Kissas · Paris Perdikaris · George J. Pappas

Modeling Human Exploration Through Resource-Rational Reinforcement Learning

Marcel Binz · Eric Schulz

Hierarchical classification at multiple operating points

Jack Valmadre

Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees

Jonathan Brophy · Daniel Lowd

Sparsity in Continuous-Depth Neural Networks

Hananeh Aliee · Till Richter · Mikhail Solonin · Ignacio Ibarra · Fabian Theis · Niki Kilbertus

Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning

Elias Frantar · Dan Alistarh

Diagonal State Spaces are as Effective as Structured State Spaces

Ankit Gupta · Albert Gu · Jonathan Berant

UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes

Alexander Kolesnikov · André Susano Pinto · Lucas Beyer · Xiaohua Zhai · Jeremiah Harmsen · Neil Houlsby

Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos

Gautam Singh · Yi-Fu Wu · Sungjin Ahn

Cluster and Aggregate: Face Recognition with Large Probe Set

Minchul Kim · Feng Liu · Anil K Jain · Xiaoming Liu

Reinforcement Learning with Automated Auxiliary Loss Search

Tairan He · Yuge Zhang · Kan Ren · Minghuan Liu · Che Wang · Weinan Zhang · Yuqing Yang · Dongsheng Li

Distribution-Informed Neural Networks for Domain Adaptation Regression

Jun Wu · Jingrui He · Sheng Wang · Kaiyu Guan · Elizabeth Ainsworth

Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?

Xiang Li · Jinghuan Shang · Srijan Das · Michael Ryoo

Association Graph Learning for Multi-Task Classification with Category Shifts

Jiayi Shen · Zehao Xiao · Xiantong Zhen · Cees Snoek · Marcel Worring

Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition

Shuai Jia · Bangjie Yin · Taiping Yao · Shouhong Ding · Chunhua Shen · Xiaokang Yang · Chao Ma

OST: Improving Generalization of DeepFake Detection via One-Shot Test-Time Training

Liang Chen · Yong Zhang · Yibing Song · Jue Wang · Lingqiao Liu

Grow and Merge: A Unified Framework for Continuous Categories Discovery

Xinwei Zhang · Jianwen Jiang · Yutong Feng · Zhi-Fan Wu · Xibin Zhao · Hai Wan · Mingqian Tang · Rong Jin · Yue Gao

Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution

Khai Nguyen · Nhat Ho

Meta-Complementing the Semantics of Short Texts in Neural Topic Models

Delvin Ce Zhang · Hady Lauw

Learning Equivariant Segmentation with Instance-Unique Querying

Wenguan Wang · James Liang · Dongfang Liu

DivBO: Diversity-aware CASH for Ensemble Learning

Yu Shen · Yupeng Lu · Yang Li · Yaofeng Tu · Wentao Zhang · Bin CUI

Towards Efficient Post-training Quantization of Pre-trained Language Models

Haoli Bai · Lu Hou · Lifeng Shang · Xin Jiang · Irwin King · Michael R Lyu

FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction

Samiul Alam · Luyang Liu · Ming Yan · Mi Zhang

How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders

Qi Zhang · Yifei Wang · Yisen Wang

Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching

Byoungjip Kim · Sungik Choi · Dasol Hwang · Moontae Lee · Honglak Lee

A Neural Corpus Indexer for Document Retrieval

Yujing Wang · Yingyan Hou · Haonan Wang · Ziming Miao · Shibin Wu · Hao Sun · Qi Chen · Yuqing Xia · Chengmin Chi · Guoshuai Zhao · Zheng Liu · Xing Xie · Hao Sun · Weiwei Deng · Qi Zhang · Mao Yang

I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification

Muhammad Ferjad Naeem · Yongqin Xian · Luc V Gool · Federico Tombari

ElasticMVS: Learning elastic part representation for self-supervised multi-view stereopsis

Jinzhi Zhang · Ruofan Tang · Zheng Cao · Jing Xiao · Ruqi Huang · LU FANG

Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts

Tao Zhong · Zhixiang Chi · Li Gu · Yang Wang · Yuanhao Yu · Jin Tang

DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations

Ximeng Sun · Ping Hu · Kate Saenko

MaskTune: Mitigating Spurious Correlations by Forcing to Explore

Saeid Asgari · Aliasghar Khani · Fereshte Khani · Ali Gholami · Linh Tran · Ali Mahdavi Amiri · Ghassan Hamarneh

Neuron with Steady Response Leads to Better Generalization

Qiang Fu · Lun Du · Haitao Mao · Xu Chen · Wei Fang · Shi Han · Dongmei Zhang

Invariance Learning based on Label Hierarchy

Shoji Toyota · Kenji Fukumizu

Bidirectional Learning for Offline Infinite-width Model-based Optimization

Can Chen · Yingxueff Zhang · Jie Fu · Xue (Steve) Liu · Mark Coates

Energy-Based Contrastive Learning of Visual Representations

Beomsu Kim · Jong Chul Ye

FR: Folded Rationalization with a Unified Encoder

Wei Liu · Haozhao Wang · Jun Wang · Ruixuan Li · Chao Yue · YuanKai Zhang

Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models

Dongkuan (DK) Xu · Subhabrata Mukherjee · Xiaodong Liu · Debadeepta Dey · Wenhui Wang · Xiang Zhang · Ahmed Awadallah · Jianfeng Gao

Unsupervised Adaptation from Repeated Traversals for Autonomous Driving

Yurong You · Cheng Perng Phoo · Katie Luo · Travis Zhang · Wei-Lun Chao · Bharath Hariharan · Mark Campbell · Kilian Weinberger

Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)

Ali Seyfi · Jean-Francois Rajotte · Raymond Ng

Unsupervised Learning of Equivariant Structure from Sequences

Takeru Miyato · Masanori Koyama · Kenji Fukumizu

An Analytical Theory of Curriculum Learning in Teacher-Student Networks

Luca Saglietti · Stefano Mannelli · Andrew Saxe

Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting

Alex Ororbia · Ankur Mali · C Lee Giles · Daniel Kifer

MAgNet: Mesh Agnostic Neural PDE Solver

Oussama Boussif · Yoshua Bengio · Loubna Benabbou · Dan Assouline

Iterative Structural Inference of Directed Graphs

Aoran Wang · Jun Pang

Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning

Wenhao Ding · Haohong Lin · Bo Li · DING ZHAO

A Unifying Framework of Off-Policy General Value Function Evaluation

Tengyu Xu · Zhuoran Yang · Zhaoran Wang · Yingbin Liang

Langevin Autoencoders for Learning Deep Latent Variable Models

Shohei Taniguchi · Yusuke Iwasawa · Wataru Kumagai · Yutaka Matsuo

SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems

Leonid Iosipoi · Anton Vakhrushev

Your Transformer May Not be as Powerful as You Expect

Shengjie Luo · Shanda Li · Shuxin Zheng · Tie-Yan Liu · Liwei Wang · Di He

GRASP: Navigating Retrosynthetic Planning with Goal-driven Policy

Yemin Yu · Ying Wei · Kun Kuang · Zhengxing Huang · Huaxiu Yao · Fei Wu

High-Order Pooling for Graph Neural Networks with Tensor Decomposition

Chenqing Hua · Guillaume Rabusseau · Jian Tang

TreeMoCo: Contrastive Neuron Morphology Representation Learning

Hanbo Chen · Jiawei Yang · Daniel Iascone · Lijuan Liu · Lei He · Hanchuan Peng · Jianhua Yao

SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos

Gamaleldin Elsayed · Aravindh Mahendran · Sjoerd van Steenkiste · Klaus Greff · Michael Mozer · Thomas Kipf

TVLT: Textless Vision-Language Transformer

Zineng Tang · Jaemin Cho · Yixin Nie · Mohit Bansal

Molecule Generation by Principal Subgraph Mining and Assembling

Xiangzhe Kong · Wenbing Huang · Zhixing Tan · Yang Liu

AttCAT: Explaining Transformers via Attentive Class Activation Tokens

Yao Qiang · Deng Pan · Chengyin Li · Xin Li · Rhongho Jang · Dongxiao Zhu

Joint Learning of 2D-3D Weakly Supervised Semantic Segmentation

Hyeokjun Kweon · Kuk-Jin Yoon

Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling

Kaitao Song · Yichong Leng · Xu Tan · Yicheng Zou · Tao Qin · Dongsheng Li

Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation

Jinlong Li · Zequn Jie · Xu Wang · Xiaolin Wei · Lin Ma

Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination

YIZHEN ZHENG · Shirui Pan · Vincent CS Lee · Yu Zheng · Philip S Yu

Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems

Jiaqi Yang · De-Chuan Zhan

Phase Transition from Clean Training to Adversarial Training

Yue Xing · Qifan Song · Guang Cheng

On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses

Anshuman Chhabra · Ashwin Sekhari · Prasant Mohapatra

LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks

Tuan Dinh · Yuchen Zeng · Ruisu Zhang · Ziqian Lin · Michael Gira · Shashank Rajput · Jy-yong Sohn · Dimitris Papailiopoulos · Kangwook Lee

Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator

Zifan Shi · Yinghao Xu · Yujun Shen · Deli Zhao · Qifeng Chen · Dit-Yan Yeung

Watermarking for Out-of-distribution Detection

Qizhou Wang · Feng Liu · Yonggang Zhang · Jing Zhang · Chen Gong · Tongliang Liu · Bo Han

Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs

Ming Jin · Yuan-Fang Li · Shirui Pan

InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model

Sidi Lu · Tao Meng · Nanyun Peng

Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks

Paolo Muratore · Sina Tafazoli · Eugenio Piasini · Alessandro Laio · Davide Zoccolan

Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations

Amit Dhurandhar · Karthikeyan Natesan Ramamurthy · Karthikeyan Shanmugam

Recruitment Strategies That Take a Chance

Gregory Kehne · Ariel Procaccia · Jingyan Wang

Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces

Mojmir Mutny · Andreas Krause

Physics-Informed Implicit Representations of Equilibrium Network Flows

Kevin D. Smith · Francesco Seccamonte · Ananthram Swami · Francesco Bullo

Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings

Dongxu Zhang · Michael Boratko · Cameron Musco · Andrew McCallum

Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints

Xinmeng Huang · Donghwan Lee · Edgar Dobriban · Hamed Hassani

A Boosting Approach to Reinforcement Learning

Nataly Brukhim · Elad Hazan · Karan Singh

Active Learning with Neural Networks: Insights from Nonparametric Statistics

Yinglun Zhu · Robert Nowak

Optimal Scaling for Locally Balanced Proposals in Discrete Spaces

Haoran Sun · Hanjun Dai · Dale Schuurmans

Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret

Orestis Papadigenopoulos · Constantine Caramanis · Sanjay Shakkottai

Effective Dimension in Bandit Problems under Censorship

Gauthier Guinet · Saurabh Amin · Patrick Jaillet

Partial Identification of Treatment Effects with Implicit Generative Models

Vahid Balazadeh Meresht · Vasilis Syrgkanis · Rahul Krishnan

A Universal Error Measure for Input Predictions Applied to Online Graph Problems

Giulia Bernardini · Alexander Lindermayr · Alberto Marchetti-Spaccamela · Nicole Megow · Leen Stougie · Michelle Sweering

Minimax Regret for Cascading Bandits

Daniel Vial · Sujay Sanghavi · Sanjay Shakkottai · R. Srikant

Defining and Characterizing Reward Gaming

Joar Skalse · Nikolaus Howe · Dmitrii Krasheninnikov · David Krueger

Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update

Georgios Piliouras · Ryann Sim · Stratis Skoulakis

Non-convex online learning via algorithmic equivalence

Udaya Ghai · Zhou Lu · Elad Hazan

Annihilation of Spurious Minima in Two-Layer ReLU Networks

Yossi Arjevani · Michael Field

On global convergence of ResNets: From finite to infinite width using linear parameterization

Raphaël Barboni · Gabriel Peyré · Francois-Xavier Vialard

GULP: a prediction-based metric between representations

Enric Boix-Adsera · Hannah Lawrence · George Stepaniants · Philippe Rigollet

A Combinatorial Perspective on the Optimization of Shallow ReLU Networks

Michael S Matena · Colin Raffel

Learning sparse features can lead to overfitting in neural networks

Leonardo Petrini · Francesco Cagnetta · Eric Vanden-Eijnden · Matthieu Wyart

Conformal Off-Policy Prediction in Contextual Bandits

Muhammad Faaiz Taufiq · Jean-Francois Ton · Rob Cornish · Yee Whye Teh · Arnaud Doucet

Movement Penalized Bayesian Optimization with Application to Wind Energy Systems

Shyam Sundhar Ramesh · Pier Giuseppe Sessa · Andreas Krause · Ilija Bogunovic

Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning

Paul Rolland · Luca Viano · Norman Schürhoff · Boris Nikolov · Volkan Cevher

The Implicit Delta Method

Nathan Kallus · James McInerney

Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers

Colin Wei · Yining Chen · Tengyu Ma

Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions

Omri Ben-Eliezer · Dan Mikulincer · Ilias Zadik

Markovian Interference in Experiments

Vivek Farias · Andrew Li · Tianyi Peng · Andrew Zheng

Whitening Convergence Rate of Coupling-based Normalizing Flows

Felix Draxler · Christoph Schnörr · Ullrich Köthe

When Does Differentially Private Learning Not Suffer in High Dimensions?

Xuechen Li · Daogao Liu · Tatsunori Hashimoto · Huseyin A. Inan · Janardhan Kulkarni · Yin-Tat Lee · Abhradeep Guha Thakurta

DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning

Quan Vuong · Aviral Kumar · Sergey Levine · Yevgen Chebotar

Are GANs overkill for NLP?

David Alvarez-Melis · Vikas Garg · Adam Kalai

Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization

Jonathan Wilton · Abigail Koay · Ryan Ko · Miao Xu · Nan Ye

Direct Advantage Estimation

Hsiao-Ru Pan · Nico Gürtler · Alexander Neitz · Bernhard Schölkopf

Simplified Graph Convolution with Heterophily

Sudhanshu Chanpuriya · Cameron Musco

GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games

Shichang Zhang · Yozen Liu · Neil Shah · Yizhou Sun

Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards

Rati Devidze · Parameswaran Kamalaruban · Adish Singla

Relational Proxies: Emergent Relationships as Fine-Grained Discriminators

ABHRA CHAUDHURI · Massimiliano Mancini · Zeynep Akata · Anjan Dutta

Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting

Yuzhou Chen · Yulia Gel · H. Vincent Poor

MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators

Jinyoung Choi · Bohyung Han

Learning Individualized Treatment Rules with Many Treatments: A Supervised Clustering Approach Using Adaptive Fusion

Haixu Ma · Donglin Zeng · Yufeng Liu

Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection

Fan Wang · Adams Wai Kin Kong

Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation

Ziyu Jiang · Xuxi Chen · Xueqin Huang · Xianzhi Du · Denny Zhou · Zhangyang Wang

Not All Bits have Equal Value: Heterogeneous Precisions via Trainable Noise

Pedro Savarese · Xin Yuan · Yanjing Li · Michael Maire

Pure Transformers are Powerful Graph Learners

Jinwoo Kim · Dat Nguyen · Seonwoo Min · Sungjun Cho · Moontae Lee · Honglak Lee · Seunghoon Hong

Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms

QIN DING · Yue Kang · Yi-Wei Liu · Thomas Chun Man Lee · Cho-Jui Hsieh · James Sharpnack

Bayesian Active Learning with Fully Bayesian Gaussian Processes

Christoffer Riis · Francisco Antunes · Frederik Hüttel · Carlos Lima Azevedo · Francisco Pereira

$k$-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension

Ziv Goldfeld · Kristjan Greenewald · Theshani Nuradha · Galen Reeves

Accelerated Projected Gradient Algorithms for Sparsity Constrained Optimization Problems

Jan Harold Alcantara · Ching-pei Lee

Sparse Gaussian Process Hyperparameters: Optimize or Integrate?

Vidhi Lalchand · Wessel Bruinsma · David Burt · Carl Edward Rasmussen

Detecting Abrupt Changes in Sequential Pairwise Comparison Data

Wanshan Li · Alessandro Rinaldo · Daren Wang

Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers

Ran Liu · Mehdi Azabou · Max Dabagia · Jingyun Xiao · Eva Dyer

Learning Fractional White Noises in Neural Stochastic Differential Equations

Anh Tong · Thanh Nguyen-Tang · Toan Tran · Jaesik Choi

On the Frequency-bias of Coordinate-MLPs

Sameera Ramasinghe · Lachlan E. MacDonald · Simon Lucey

Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors

Ravid Shwartz-Ziv · Micah Goldblum · Hossein Souri · Sanyam Kapoor · Chen Zhu · Yann LeCun · Andrew Wilson

GAUDI: A Neural Architect for Immersive 3D Scene Generation

Miguel Angel Bautista · Pengsheng Guo · Samira Abnar · Walter Talbott · Alexander Toshev · Zhuoyuan Chen · Laurent Dinh · Shuangfei Zhai · Hanlin Goh · Daniel Ulbricht · Afshin Dehghan · Joshua Susskind

Learning Debiased Classifier with Biased Committee

Nayeong Kim · SEHYUN HWANG · Sungsoo Ahn · Jaesik Park · Suha Kwak

PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning

Hanhan Zhou · Tian Lan · Vaneet Aggarwal

Recurrent Memory Transformer

Aydar Bulatov · Yury Kuratov · Mikhail Burtsev

Fast Vision Transformers with HiLo Attention

Zizheng Pan · Jianfei Cai · Bohan Zhuang

Dynamic Sparse Network for Time Series Classification: Learning What to “See”

Qiao Xiao · Boqian Wu · Yu Zhang · Shiwei Liu · Mykola Pechenizkiy · Elena Mocanu · Decebal Constantin Mocanu

Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution

Xueying Ding · Lingxiao Zhao · Leman Akoglu

Extrapolative Continuous-time Bayesian Neural Network for Fast Training-free Test-time Adaptation

Hengguan Huang · Xiangming Gu · Hao Wang · Chang Xiao · Hongfu Liu · Ye Wang

Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation

Haoyu Peter Wang · Nan Wu · Hang Yang · Cong Hao · Pan Li

Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers

Nathan Tsoi · Kate Candon · Deyuan Li · Yofti Milkessa · Marynel Vázquez

Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?

Yimeng Min · Frederik Wenkel · Michael Perlmutter · Guy Wolf

Unsupervised Learning From Incomplete Measurements for Inverse Problems

Julián Tachella · Dongdong Chen · Mike Davies

On the Double Descent of Random Features Models Trained with SGD

Fanghui Liu · Johan Suykens · Volkan Cevher

Generalization Properties of NAS under Activation and Skip Connection Search

Zhenyu Zhu · Fanghui Liu · Grigorios Chrysos · Volkan Cevher

Neur2SP: Neural Two-Stage Stochastic Programming

Rahul Mihir Patel · Justin Dumouchelle · Elias Khalil · Merve Bodur

Receding Horizon Inverse Reinforcement Learning

Yiqing Xu · Wei Gao · David Hsu

Neural Attentive Circuits

Martin Weiss · Nasim Rahaman · Francesco Locatello · Chris Pal · Yoshua Bengio · Bernhard Schölkopf · Erran Li Li · Nicolas Ballas

Interaction Modeling with Multiplex Attention

Fan-Yun Sun · Isaac Kauvar · Ruohan Zhang · Jiachen Li · Mykel J Kochenderfer · Jiajun Wu · Nick Haber

Measures of Information Reflect Memorization Patterns

Rachit Bansal · Danish Pruthi · Yonatan Belinkov

Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks

Mansheej Paul · Brett Larsen · Surya Ganguli · Jonathan Frankle · Gintare Karolina Dziugaite

Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent

Yiping Lu · Jose Blanchet · Lexing Ying

Blessing of Depth in Linear Regression: Deeper Models Have Flatter Landscape Around the True Solution

Jianhao Ma · Salar Fattahi

Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions

Courtney Paquette · Elliot Paquette · Ben Adlam · Jeffrey Pennington

Proximal Learning With Opponent-Learning Awareness

Stephen Zhao · Chris Lu · Roger Grosse · Jakob Foerster

ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization

Siqi Shen · Mengwei Qiu · Jun Liu · Weiquan Liu · Yongquan Fu · Xinwang Liu · Cheng Wang

Joint Entropy Search For Maximally-Informed Bayesian Optimization

Carl Hvarfner · Frank Hutter · Luigi Nardi

Can Push-forward Generative Models Fit Multimodal Distributions?

Antoine Salmona · Valentin De Bortoli · Julie Delon · Agnes Desolneux

Learning Interface Conditions in Domain Decomposition Solvers

Ali Taghibakhshi · Nicolas Nytko · Tareq Uz Zaman · Scott MacLachlan · Luke Olson · Matthew West

Chaotic Dynamics are Intrinsic to Neural Network Training with SGD

Luis Herrmann · Maximilian Granz · Tim Landgraf

An In-depth Study of Stochastic Backpropagation

Jun Fang · Mingze Xu · Hao Chen · Bing Shuai · Zhuowen Tu · Joseph Tighe

Automatic Differentiation of Programs with Discrete Randomness

Gaurav Arya · Moritz Schauer · Frank Schäfer · Christopher Rackauckas

Weakly Supervised Representation Learning with Sparse Perturbations

Kartik Ahuja · Jason Hartford · Yoshua Bengio

Domain Generalization without Excess Empirical Risk

Ozan Sener · Vladlen Koltun

Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions

Andrew Jesson · Alyson Douglas · Peter Manshausen · Maëlys Solal · Nicolai Meinshausen · Philip Stier · Yarin Gal · Uri Shalit

Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs

Julia Costacurta · Lea Duncker · Blue Sheffer · Winthrop Gillis · Caleb Weinreb · Jeffrey Markowitz · Sandeep R Datta · Alex Williams · Scott Linderman

Handcrafted Backdoors in Deep Neural Networks

Sanghyun Hong · Nicholas Carlini · Alexey Kurakin

Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve

Giannis Daras · Negin Raoof · Zoi Gkalitsiou · Alex Dimakis

Representing Spatial Trajectories as Distributions

Didac Suris Coll-Vinent · Carl Vondrick

Exact Solutions of a Deep Linear Network

Liu Ziyin · Botao Li · Xiangming Meng

Systematic improvement of neural network quantum states using Lanczos

Hongwei Chen · Douglas Hendry · Phillip Weinberg · Adrian Feiguin

Score-Based Diffusion meets Annealed Importance Sampling

Arnaud Doucet · Will Grathwohl · Alexander Matthews · Heiko Strathmann

Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits

Tong Mu · Yash Chandak · Tatsunori Hashimoto · Emma Brunskill

On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting

Tomasz Korbak · Hady Elsahar · Germán Kruszewski · Marc Dymetman

Learning General World Models in a Handful of Reward-Free Deployments

Yingchen Xu · Jack Parker-Holder · Aldo Pacchiano · Philip Ball · Oleh Rybkin · S Roberts · Tim Rocktäschel · Edward Grefenstette

LISA: Learning Interpretable Skill Abstractions from Language

Divyansh Garg · Skanda Vaidyanath · Kuno Kim · Jiaming Song · Stefano Ermon

SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections

Mark Boss · Andreas Engelhardt · Abhishek Kar · Yuanzhen Li · Deqing Sun · Jonathan Barron · Hendrik PA Lensch · Varun Jampani

Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks

Chenxiao Yang · Qitian Wu · Junchi Yan

FourierNets enable the design of highly non-local optical encoders for computational imaging

Diptodip Deb · Zhenfei Jiao · Ruth Sims · Alex Chen · Michael Broxton · Misha B Ahrens · Kaspar Podgorski · Srinivas C Turaga

Learning to Scaffold: Optimizing Model Explanations for Teaching

Patrick Fernandes · Marcos Treviso · Danish Pruthi · André Martins · Graham Neubig

SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration

Antoine Guedon · Pascal Monasse · Vincent Lepetit

VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids

Albert Pumarola · Artsiom Sanakoyeu · Lior Yariv · Ali Thabet · Yaron Lipman

A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension

Binh T. Nguyen · Bertrand Thirion · Sylvain Arlot

Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT

Cuneyt G Akcora · Murat Kantarcioglu · Yulia Gel · Baris Coskunuzer

A Consolidated Cross-Validation Algorithm for Support Vector Machines via Data Reduction

Boxiang Wang · Archer Yang

RISE: Robust Individualized Decision Learning with Sensitive Variables

Xiaoqing Tan · Zhengling Qi · Christopher Seymour · Lu Tang

Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning

HYUNWOOK KANG · Taehwan Kwon · Jinkyoo Park · James R. Morrison

Tractable Optimality in Episodic Latent MABs

Jeongyeol Kwon · Yonathan Efroni · Constantine Caramanis · Shie Mannor

Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness

Amin Jaber · Adele Ribeiro · Jiji Zhang · Elias Bareinboim

Non-Gaussian Tensor Programs

Eugene Golikov · Greg Yang

Nonparametric Uncertainty Quantification for Single Deterministic Neural Network

Nikita Kotelevskii · Aleksandr Artemenkov · Kirill Fedyanin · Fedor Noskov · Alexander Fishkov · Artem Shelmanov · Artem Vazhentsev · Aleksandr Petiushko · Maxim Panov

Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization

Junchi YANG · Xiang Li · Niao He

DP-PCA: Statistically Optimal and Differentially Private PCA

Xiyang Liu · Weihao Kong · Prateek Jain · Sewoong Oh

DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning

Archana Bura · Aria HasanzadeZonuzy · Dileep Kalathil · Srinivas Shakkottai · Jean-Francois Chamberland

Acceleration in Distributed Sparse Regression

Marie Maros · Gesualdo Scutari

Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox

Abdurakhmon Sadiev · Dmitry Kovalev · Peter Richtarik

Chefs' Random Tables: Non-Trigonometric Random Features

Valerii Likhosherstov · Krzysztof M Choromanski · Kumar Avinava Dubey · Frederick Liu · Tamas Sarlos · Adrian Weller

Rapid Model Architecture Adaption for Meta-Learning

Yiren Zhao · Xitong Gao · I Shumailov · Nicolo Fusi · Robert Mullins

UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification

Yanbo Xu · Alind Khare · Glenn Matlin · Monish Ramadoss · Rishikesan Kamaleswaran · Chao Zhang · Alexey Tumanov

Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers

Albert Qiaochu Jiang · Wenda Li · Szymon Tworkowski · Konrad Czechowski · Tomasz Odrzygóźdź · Piotr Miłoś · Yuhuai Wu · Mateja Jamnik

MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction

Zehao Yu · Songyou Peng · Michael Niemeyer · Torsten Sattler · Andreas Geiger

Implicit Warping for Animation with Image Sets

Arun Mallya · Ting-Chun Wang · Ming-Yu Liu

Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space

Jinghuan Shang · Srijan Das · Michael Ryoo

VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts

Hangbo Bao · Wenhui Wang · Li Dong · Qiang Liu · Owais Khan Mohammed · Kriti Aggarwal · Subhojit Som · Songhao Piao · Furu Wei

Few-shot Image Generation via Adaptation-Aware Kernel Modulation

Yunqing Zhao · Keshigeyan Chandrasegaran · Milad Abdollahzadeh · Ngai-Man (Man) Cheung

Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models

Manli Shu · Weili Nie · De-An Huang · Zhiding Yu · Tom Goldstein · Anima Anandkumar · Chaowei Xiao

TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers

Hyeong Kyu Choi · Joonmyung Choi · Hyunwoo Kim

SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG

Reinmar Kobler · Jun-ichiro Hirayama · Qibin Zhao · Motoaki Kawanabe

Locally Hierarchical Auto-Regressive Modeling for Image Generation

Tackgeun You · Saehoon Kim · Chiheon Kim · Doyup Lee · Bohyung Han

Deep Generative Model for Periodic Graphs

Shiyu Wang · Xiaojie Guo · Liang Zhao

Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training

Renrui Zhang · Ziyu Guo · Peng Gao · Rongyao Fang · Bin Zhao · Dong Wang · Yu Qiao · Hongsheng Li

AutoST: Towards the Universal Modeling of Spatio-temporal Sequences

Jianxin Li · Shuai Zhang · Hui Xiong · Haoyi Zhou

Egocentric Video-Language Pretraining

Kevin Qinghong Lin · Jinpeng Wang · Mattia Soldan · Michael Wray · Rui Yan · Eric Z. XU · Difei Gao · Rong-Cheng Tu · Wenzhe Zhao · Weijie Kong · Chengfei Cai · WANG HongFa · Dima Damen · Bernard Ghanem · Wei Liu · Mike Zheng Shou

Learning Active Camera for Multi-Object Navigation

Peihao Chen · Dongyu Ji · Kunyang Lin · Weiwen Hu · Wenbing Huang · Thomas Li · Mingkui Tan · Chuang Gan

Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors

Eric Wu · Nora Brackbill · Alexander Sher · Alan Litke · Eero Simoncelli · E.J. Chichilnisky

Assaying Out-Of-Distribution Generalization in Transfer Learning

Florian Wenzel · Andrea Dittadi · Peter Gehler · Carl-Johann Simon-Gabriel · Max Horn · Dominik Zietlow · David Kernert · Chris Russell · Thomas Brox · Bernt Schiele · Bernhard Schölkopf · Francesco Locatello

Not too little, not too much: a theoretical analysis of graph (over)smoothing

Nicolas Keriven

The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models

Conglong Li · Minjia Zhang · Yuxiong He

Squeezeformer: An Efficient Transformer for Automatic Speech Recognition

Sehoon Kim · Amir Gholami · Albert Shaw · Nicholas Lee · Karttikeya Mangalam · Jitendra Malik · Michael Mahoney · Kurt Keutzer

GREED: A Neural Framework for Learning Graph Distance Functions

Rishabh Ranjan · Siddharth Grover · Sourav Medya · Venkatesan Chakaravarthy · Yogish Sabharwal · Sayan Ranu

Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients

Hualin Zhang · Huan Xiong · Bin Gu

Does Momentum Change the Implicit Regularization on Separable Data?

Bohan Wang · Qi Meng · Huishuai Zhang · Ruoyu Sun · Wei Chen · Zhi-Ming Ma · Tie-Yan Liu

Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs

Jonathan Kelner · Frederic Koehler · Raghu Meka · Dhruv Rohatgi

Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning

Rujie Zhong · Duohan Zhang · Lukas Schäfer · Stefano Albrecht · Josiah Hanna

Redundant representations help generalization in wide neural networks

Diego Doimo · Aldo Glielmo · Sebastian Goldt · Alessandro Laio

Explainability Via Causal Self-Talk

Nicholas Roy · Junkyung Kim · Neil Rabinowitz

Learn what matters: cross-domain imitation learning with task-relevant embeddings

Tim Franzmeyer · Philip Torr · João Henriques

Learning to Compare Nodes in Branch and Bound with Graph Neural Networks

Abdel Ghani Labassi · Didier Chetelat · Andrea Lodi

MoCoDA: Model-based Counterfactual Data Augmentation

Silviu Pitis · Elliot Creager · Ajay Mandlekar · Animesh Garg

Subsidiary Prototype Alignment for Universal Domain Adaptation

Jogendra Nath Kundu · Suvaansh Bhambri · Akshay R Kulkarni · Hiran Sarkar · Varun Jampani · Venkatesh Babu R

A Fast Post-Training Pruning Framework for Transformers

Woosuk Kwon · Sehoon Kim · Michael Mahoney · Joseph Hassoun · Kurt Keutzer · Amir Gholami

Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior

Zoe Ashwood · Aditi Jha · Jonathan Pillow

NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos

Yi-Ling Qiao · Alexander Gao · Ming Lin

Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights

Konstantin Schürholt · Boris Knyazev · Xavier Giró-i-Nieto · Damian Borth

DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning

Siqi Xu · Lin Liu · Zhonghua Liu

Power and limitations of single-qubit native quantum neural networks

Zhan Yu · Hongshun Yao · Mujin Li · Xin Wang

SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning

Jianhong Wang · Yuan Zhang · Yunjie Gu · Tae-Kyun Kim

Heatmap Distribution Matching for Human Pose Estimation

Haoxuan Qu · Li Xu · Yujun Cai · Lin Geng Foo · Jun Liu

Tensor Program Optimization with Probabilistic Programs

Junru Shao · Xiyou Zhou · Siyuan Feng · Bohan Hou · Ruihang Lai · Hongyi Jin · Wuwei Lin · Masahiro Masuda · Cody Hao Yu · Tianqi Chen

Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning

Minjong Yoo · SangWoo Cho · Honguk Woo

Benign Overfitting in Two-layer Convolutional Neural Networks

Yuan Cao · Zixiang Chen · Misha Belkin · Quanquan Gu

Task Discovery: Finding the Tasks that Neural Networks Generalize on

Andrei Atanov · Andrei Filatov · Teresa Yeo · Ajay Sohmshetty · Amir Zamir

Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions

Fenglin Liu · Bang Yang · Chenyu You · Xian Wu · Shen Ge · Zhangdaihong Liu · Xu Sun · Yang Yang · David Clifton

NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis

Jian Liang · Chenfei Wu · Xiaowei Hu · Zhe Gan · Jianfeng Wang · Lijuan Wang · Zicheng Liu · Yuejian Fang · Nan Duan

MORA: Improving Ensemble Robustness Evaluation with Model Reweighing Attack

yunrui yu · Xitong Gao · Cheng-Zhong Xu

Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models

Zijian Zhang · Zhou Zhao · Zhijie Lin

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models

Erdun Gao · Ignavier Ng · Mingming Gong · Li Shen · Wei Huang · Tongliang Liu · Kun Zhang · Howard Bondell

Domain Adaptation under Open Set Label Shift

Saurabh Garg · Sivaraman Balakrishnan · Zachary Lipton

Lethal Dose Conjecture on Data Poisoning

Wenxiao Wang · Alexander Levine · Soheil Feizi

Emergent Communication: Generalization and Overfitting in Lewis Games

Mathieu Rita · Corentin Tallec · Paul Michel · Jean-Bastien Grill · Olivier Pietquin · Emmanuel Dupoux · Florian Strub

Asymptotic Properties for Bayesian Neural Network in Besov Space

Kyeongwon Lee · Jaeyong Lee

On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

Sanyam Kapoor · Wesley Maddox · Pavel Izmailov · Andrew Wilson

Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Kushal Tirumala · Aram Markosyan · Luke Zettlemoyer · Armen Aghajanyan

Multi-fidelity Monte Carlo: a pseudo-marginal approach

Diana Cai · Ryan Adams

Near-Optimal Correlation Clustering with Privacy

Vincent Cohen-Addad · Chenglin Fan · Silvio Lattanzi · Slobodan Mitrovic · Ashkan Norouzi-Fard · Nikos Parotsidis · Jakub Tarnawski

Rethinking Variational Inference for Probabilistic Programs with Stochastic Support

Tim Reichelt · Luke Ong · Thomas Rainforth

Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams

Sergey Denisov · H. Brendan McMahan · John Rush · Adam Smith · Abhradeep Guha Thakurta

Positively Weighted Kernel Quadrature via Subsampling

Satoshi Hayakawa · Harald Oberhauser · Terry Lyons

On the non-universality of deep learning: quantifying the cost of symmetry

Emmanuel Abbe · Enric Boix-Adsera

Globally Convergent Policy Search for Output Estimation

Jack Umenberger · Max Simchowitz · Juan Perdomo · Kaiqing Zhang · Russ Tedrake

Optimistic Tree Searches for Combinatorial Black-Box Optimization

Cedric Malherbe · Antoine Grosnit · Rasul Tutunov · Haitham Bou Ammar · Jun Wang

Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints

Jiajin Li · Sirui Lin · Jose Blanchet · Viet Anh Nguyen

IMED-RL: Regret optimal learning of ergodic Markov decision processes

Fabien Pesquerel · Odalric-Ambrym Maillard

Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits

Gergely Neu · Iuliia Olkhovskaia · Matteo Papini · Ludovic Schwartz

KSD Aggregated Goodness-of-fit Test

Antonin Schrab · Benjamin Guedj · Arthur Gretton

Latent Hierarchical Causal Structure Discovery with Rank Constraints

Biwei Huang · Charles Jia Han Low · Feng Xie · Clark Glymour · Kun Zhang

In Differential Privacy, There is Truth: on Vote-Histogram Leakage in Ensemble Private Learning

JIAQI WANG · Roei Schuster · I Shumailov · David Lie · Nicolas Papernot

Explicit Tradeoffs between Adversarial and Natural Distributional Robustness

Mazda Moayeri · Kiarash Banihashem · Soheil Feizi

A Mean-Field Game Approach to Cloud Resource Management with Function Approximation

Weichao Mao · Haoran Qiu · Chen Wang · Hubertus Franke · Zbigniew Kalbarczyk · Ravishankar Iyer · Tamer Basar

On Privacy and Personalization in Cross-Silo Federated Learning

Ken Liu · Shengyuan Hu · Steven Wu · Virginia Smith

Byzantine Spectral Ranking

Arnhav Datar · Arun Rajkumar · John Augustine

Learning to Follow Instructions in Text-Based Games

Mathieu Tuli · Andrew Li · Pashootan Vaezipoor · Toryn Klassen · Scott Sanner · Sheila McIlraith

Convergent Representations of Computer Programs in Human and Artificial Neural Networks

Shashank Srikant · Ben Lipkin · Anna Ivanova · Evelina Fedorenko · Una-May O'Reilly

Emergence of Hierarchical Layers in a Single Sheet of Self-Organizing Spiking Neurons

Paul Bertens · Seong-Whan Lee

Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence

Rahul Jain · Georgios Piliouras · Ryann Sim

Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts

Maria-Florina Balcan · Siddharth Prasad · Tuomas Sandholm · Ellen Vitercik

Exploring the Whole Rashomon Set of Sparse Decision Trees

Rui Xin · Chudi Zhong · Zhi Chen · Takuya Takagi · Margo Seltzer · Cynthia Rudin

Robust Rent Division

Dominik Peters · Ariel Procaccia · David Zhu

A Classification of $G$-invariant Shallow Neural Networks

Devanshu Agrawal · James Ostrowski

Polynomial time guarantees for the Burer-Monteiro method

Diego Cifuentes · Ankur Moitra

A sharp NMF result with applications in network modeling

Jiashun Jin

Uncoupled Learning Dynamics with $O(\log T)$ Swap Regret in Multiplayer Games

Ioannis Anagnostides · Gabriele Farina · Christian Kroer · Chung-Wei Lee · Haipeng Luo · Tuomas Sandholm

Average Sensitivity of Euclidean k-Clustering

Yuichi Yoshida · Shinji Ito

A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions

Damek Davis · Dmitriy Drusvyatskiy · Yin Tat Lee · Swati Padmanabhan · Guanghao Ye

Dynamic pricing and assortment under a contextual MNL demand

Noemie Perivier · Vineet Goyal

Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion

Jialun Zhang · Hong-Ming Chiu · Richard Y Zhang

Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)

Zhenyu Zhu · Fanghui Liu · Grigorios Chrysos · Volkan Cevher

Performative Power

Moritz Hardt · Meena Jagadeesan · Celestine Mendler-Dünner

Is Sortition Both Representative and Fair?

Soroush Ebadian · Gregory Kehne · Evi Micha · Ariel Procaccia · Nisarg Shah

Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions

Kuan-Lin Chen · Harinath Garudadri · Bhaskar D Rao

Nearly-Tight Bounds for Testing Histogram Distributions

Clément L Canonne · Ilias Diakonikolas · Daniel Kane · Sihan Liu

Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants

Andrew M. Childs · Tongyang Li · Jin-Peng Liu · Chunhao Wang · Ruizhe Zhang

A consistently adaptive trust-region method

Fadi Hamad · Oliver Hinder

Single-pass Streaming Lower Bounds for Multi-armed Bandits Exploration with Instance-sensitive Sample Complexity

Sepehr Assadi · Chen Wang

On Infinite Separations Between Simple and Optimal Mechanisms

Alexandros Psomas · Ariel Schvartzman Cohenca · S. Weinberg

Near-Optimal Private and Scalable $k$-Clustering

Vincent Cohen-Addad · Alessandro Epasto · Vahab Mirrokni · Shyam Narayanan · Peilin Zhong

Asymptotics of smoothed Wasserstein distances in the small noise regime

Yunzi Ding · Jonathan Niles-Weed

Finite-Sample Maximum Likelihood Estimation of Location

Shivam Gupta · Jasper Lee · Eric Price · Paul Valiant

High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation

Jimmy Ba · Murat Erdogdu · Taiji Suzuki · Zhichao Wang · Denny Wu · Greg Yang

Fast Distance Oracles for Any Symmetric Norm

Yichuan Deng · Zhao Song · OMRI WEINSTEIN · Ruizhe Zhang

Momentum Aggregation for Private Non-convex ERM

Hoang Tran · Ashok Cutkosky

Differentially Private Generalized Linear Models Revisited

Raman Arora · Raef Bassily · Cristóbal Guzmán · Michael Menart · Enayat Ullah

Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization

Idan Amir · Roi Livni · Nati Srebro

A Simple and Optimal Policy Design for Online Learning with Safety against Heavy-tailed Risk

David Simchi-Levi · Zeyu Zheng · Feng Zhu

Optimal and Adaptive Monteiro-Svaiter Acceleration

Yair Carmon · Danielle Hausler · Arun Jambulapati · Yujia Jin · Aaron Sidford

Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis

Anton Plaksin · Stepan Martyanov

Falsification before Extrapolation in Causal Effect Estimation

Zeshan M Hussain · Michael Oberst · Ming-Chieh Shih · David Sontag

Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem

Raunak Kumar · Robert Kleinberg

Bezier Gaussian Processes for Tall and Wide Data

Martin Jørgensen · Michael A Osborne

A Characterization of Semi-Supervised Adversarially Robust PAC Learnability

Idan Attias · Steve Hanneke · Yishay Mansour

Fairness in Federated Learning via Core-Stability

Bhaskar Ray Chaudhury · Linyi Li · Mintong Kang · Bo Li · Ruta Mehta

A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks

Mingrui Liu · Zhenxun Zhuang · Yunwen Lei · Chunyang Liao

Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power

Binghui Li · Jikai Jin · Han Zhong · John Hopcroft · Liwei Wang

Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit

Boaz Barak · Benjamin Edelman · Surbhi Goel · Sham Kakade · Eran Malach · Cyril Zhang

On the consistent estimation of optimal Receiver Operating Characteristic (ROC) curve

Renxiong Liu · Yunzhang Zhu

Iron: Private Inference on Transformers

Meng Hao · Hongwei Li · Hanxiao Chen · Pengzhi Xing · Guowen Xu · Tianwei Zhang

Unsupervised Learning under Latent Label Shift

Manley Roberts · Pranav Mani · Saurabh Garg · Zachary Lipton

Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets

Chinmay Maheshwari · Shankar Sastry · Eric Mazumdar

Tsetlin Machine for Solving Contextual Bandit Problems

Raihan Seraj · Jivitesh Sharma · Ole-Christoffer Granmo

Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding

Ruipeng Zhang · Chenning Yu · Jingkai Chen · Chuchu Fan · Sicun Gao

Mutual Information Divergence: A Unified Metric for Multimodal Generative Models

Jin-Hwa Kim · Yunji Kim · Jiyoung Lee · Kang Min Yoo · Sang-Woo Lee

Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection

Tianyu Wang · Xiaowei Hu · Zhengzhe LIU · Chi-Wing Fu

AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning

Tao Yang · JInghao Deng · Xiaojun Quan · Qifan Wang · Shaoliang Nie

Trading off Image Quality for Robustness is not Necessary with Regularized Deterministic Autoencoders

Amrutha Saseendran · Kathrin Skubch · Stefan Falkner · Margret Keuper

Training stochastic stabilized supralinear networks by dynamics-neutral growth

Wayne Soo · Mate Lengyel

Using Mixup as a Regularizer Can Surprisingly Improve Accuracy & Out-of-Distribution Robustness

Francesco Pinto · Harry Yang · Ser Nam Lim · Philip Torr · Puneet Dokania

Rashomon Capacity: A Metric for Predictive Multiplicity in Classification

Hsiang Hsu · Flavio Calmon

Parametrically Retargetable Decision-Makers Tend To Seek Power

Alex Turner · Prasad Tadepalli

Structured Recognition for Generative Models with Explaining Away

Changmin Yu · Hugo Soulat · Neil Burgess · Maneesh Sahani

A Continuous Time Framework for Discrete Denoising Models

Andrew Campbell · Joe Benton · Valentin De Bortoli · Thomas Rainforth · George Deligiannidis · Arnaud Doucet

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s

Felix Chern · Blake Hechtman · Andy Davis · Ruiqi Guo · David Majnemer · Sanjiv Kumar

Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments

Hugo Caselles-Dupré · Olivier Sigaud · Mohamed CHETOUANI

Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Chitwan Saharia · William Chan · Saurabh Saxena · Lala Li · Jay Whang · Emily Denton · Kamyar Ghasemipour · Raphael Gontijo Lopes · Burcu Karagol Ayan · Tim Salimans · Jonathan Ho · David Fleet · Mohammad Norouzi

An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

Xiu-Shen Wei · H.-Y. Xu · Faen Zhang · Yuxin Peng · Wei Zhou

Emergent Graphical Conventions in a Visual Communication Game

Shuwen Qiu · Sirui Xie · Lifeng Fan · Tao Gao · Jungseock Joo · Song-Chun Zhu · Yixin Zhu

A Geometric Perspective on Variational Autoencoders

Clément Chadebec · Stephanie Allassonniere

Semantic Probabilistic Layers for Neuro-Symbolic Learning

Kareem Ahmed · Stefano Teso · Kai-Wei Chang · Guy Van den Broeck · Antonio Vergari

DevFly: Bio-Inspired Development of Binary Connections for Locality Preserving Sparse Codes

Tianqi Wei · Rana Alkhoury Maroun · Qinghai Guo · Barbara Webb

Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork

Haotao Wang · Junyuan Hong · Aston Zhang · Jiayu Zhou · Zhangyang Wang

NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation

Taesik Gong · Jongheon Jeong · Taewon Kim · Yewon Kim · Jinwoo Shin · Sung-Ju Lee

Periodic Graph Transformers for Crystal Material Property Prediction

Keqiang Yan · Yi Liu · Yuchao Lin · Shuiwang Ji

MCVD - Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation

Vikram Voleti · Alexia Jolicoeur-Martineau · Chris Pal

Approximation with CNNs in Sobolev Space: with Applications to Classification

Guohao Shen · Yuling Jiao · Yuanyuan Lin · Jian Huang

MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples

Jinyuan Jia · Wenjie Qu · Neil Gong

Theoretically Provable Spiking Neural Networks

Shao-Qun Zhang · Zhi-Hua Zhou

CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers

Ming Ding · Wendi Zheng · Wenyi Hong · Jie Tang

ReCo: Retrieve and Co-segment for Zero-shot Transfer

Gyungin Shin · Weidi Xie · Samuel Albanie

Online Decision Mediation

Daniel Jarrett · Alihan Hüyük · Mihaela van der Schaar

Collaborative Decision Making Using Action Suggestions

Dylan Asmar · Mykel J Kochenderfer

When does dough become a bagel? Analyzing the remaining mistakes on ImageNet

Vijay Vasudevan · Benjamin Caine · Raphael Gontijo Lopes · Sara Fridovich-Keil · Rebecca Roelofs

Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis

Taiki Miyagawa

Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization

Kun Yuan · Xinmeng Huang · Yiming Chen · Xiaohan Zhang · Yingya Zhang · Pan Pan

Differentially Private Linear Sketches: Efficient Implementations and Applications

Fuheng Zhao · Dan Qiao · Rachel Redberg · Divyakant Agrawal · Amr El Abbadi · Yu-Xiang Wang

Pruning has a disparate impact on model accuracy

Cuong Tran · Ferdinando Fioretto · Jung-Eun Kim · Rakshit Naidu

Differentiable Analog Quantum Computing for Optimization and Control

Jiaqi Leng · Yuxiang Peng · Yi-Ling Qiao · Ming Lin · Xiaodi Wu

CCCP is Frank-Wolfe in disguise

Alp Yurtsever · Suvrit Sra

Towards Optimal Communication Complexity in Distributed Non-Convex Optimization

Kumar Kshitij Patel · Lingxiao Wang · Blake Woodworth · Brian Bullins · Nati Srebro

Better SGD using Second-order Momentum

Hoang Tran · Ashok Cutkosky

Data-Efficient Structured Pruning via Submodular Optimization

Marwa El Halabi · Suraj Srinivas · Simon Lacoste-Julien

Safe Opponent-Exploitation Subgame Refinement

Mingyang Liu · Chengjie Wu · Qihan Liu · Yansen Jing · Jun Yang · Pingzhong Tang · Chongjie Zhang

Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning

Dingwen Kong · Lin Yang

Efficient Aggregated Kernel Tests using Incomplete $U$-statistics

Antonin Schrab · Ilmun Kim · Benjamin Guedj · Arthur Gretton

Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity

Arthur Jacot · Eugene Golikov · Clement Hongler · Franck Gabriel

Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning

Fuying Wang · Yuyin Zhou · Shujun WANG · Varut Vardhanabhuti · Lequan Yu

Structure-Preserving 3D Garment Modeling with Neural Sewing Machines

Xipeng Chen · Guangrun Wang · Dizhong Zhu · Xiaodan Liang · Philip Torr · Liang Lin

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

Shunyu Yao · Howard Chen · John Yang · Karthik Narasimhan

Few-shot Learning for Feature Selection with Hilbert-Schmidt Independence Criterion

Atsutoshi Kumagai · Tomoharu Iwata · Yasutoshi Ida · Yasuhiro Fujiwara

Training and Inference on Any-Order Autoregressive Models the Right Way

Andy Shih · Dorsa Sadigh · Stefano Ermon

BagFlip: A Certified Defense Against Data Poisoning

Yuhao Zhang · Aws Albarghouthi · Loris D'Antoni

Dynamic Learning in Large Matching Markets

Anand Kalvit · Assaf Zeevi

How Powerful are K-hop Message Passing Graph Neural Networks

Jiarui Feng · Yixin Chen · Fuhai Li · Anindya Sarkar · Muhan Zhang

Global Convergence of Direct Policy Search for State-Feedback $\mathcal{H}_\infty$ Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential

Xingang Guo · Bin Hu

CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains

Bonifaz Stuhr · Johann Haselberger · Julian Gebele

PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding

Minghao Xu · Zuobai Zhang · Jiarui Lu · Zhaocheng Zhu · Yangtian Zhang · Ma Chang · Runcheng Liu · Jian Tang

OpenFWI: Large-scale Multi-structural Benchmark Datasets for Full Waveform Inversion

Chengyuan Deng · Shihang Feng · Hanchen Wang · Xitong Zhang · Peng Jin · Yinan Feng · Qili Zeng · Yinpeng Chen · Youzuo Lin

PDEBench: An Extensive Benchmark for Scientific Machine Learning

Makoto Takamoto · Timothy Praditia · Raphael Leiteritz · Daniel MacKinlay · Francesco Alesiani · Dirk Pflüger · Mathias Niepert

AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels

Nicholas Roberts · Xintong Li · Tzu-Heng Huang · Dyah Adila · Spencer Schoenberg · Cheng-Yu Liu · Lauren Pick · Haotian Ma · Aws Albarghouthi · Frederic Sala

Why do tree-based models still outperform deep learning on typical tabular data?

Leo Grinsztajn · Edouard Oyallon · Gael Varoquaux

A Benchmark for Compositional Visual Reasoning

Aimen Zerroug · Mohit Vaishnav · Julien Colin · Sebastian Musslick · Thomas Serre

SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning

Changan Chen · Carl Schissler · Sanchit Garg · Philip Kobernik · Alexander Clegg · Paul Calamia · Dhruv Batra · Philip Robinson · Kristen Grauman

PeRFception: Perception using Radiance Fields

Yoonwoo Jeong · Seungjoo Shin · Junha Lee · Chris Choy · Anima Anandkumar · Minsu Cho · Jaesik Park

Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world

Eugene Vinitsky · Nathan Lichtlé · Xiaomeng Yang · Brandon Amos · Jakob Foerster

VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web

Andrei Manolache · Florin Brad · Antonio Barbalau · Radu Tudor Ionescu · Marius Popescu

FACT: Learning Governing Abstractions Behind Integer Sequences

Peter Belcak · Ard Kastrati · Flavio Schenker · Roger Wattenhofer

Geoclidean: Few-Shot Generalization in Euclidean Geometry

Joy Hsu · Jiajun Wu · Noah Goodman

MBW: Multi-view Bootstrapping in the Wild

Mosam Dabhi · Chaoyang Wang · Tim Clifford · László Jeni · Ian Fasel · Simon Lucey

HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions

Lingjiao Chen · Zhihua Jin · Evan Sabri Eyuboglu · Christopher Ré · Matei Zaharia · James Zou

Change Event Dataset for Discovery from Spatio-temporal Remote Sensing Imagery

Utkarsh Mall · Bharath Hariharan · Kavita Bala

3DOS: Towards 3D Open Set Learning - Benchmarking and Understanding Semantic Novelty Detection on Point Clouds

Antonio Alliegro · Francesco Cappio Borlino · Tatiana Tommasi

K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions

Dong-Hee Paek · SEUNG-HYUN KONG · Kevin Tirta Wijaya

TempEL: Linking Dynamically Evolving and Newly Emerging Entities

Klim Zaporojets · Lucie-Aimée Kaffee · Johannes Deleu · Thomas Demeester · Chris Develder · Isabelle Augenstein

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability

Jonathan Crabbé · Alicia Curth · Ioana Bica · Mihaela van der Schaar

Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark

Jiaxi Gu · Xiaojun Meng · Guansong Lu · Lu Hou · Niu Minzhe · Xiaodan Liang · Lewei Yao · Runhui Huang · Wei Zhang · Xin Jiang · Chunjing XU · Hang Xu

LIPS - Learning Industrial Physical Simulation benchmark suite

Milad LEYLI ABADI · Antoine Marot · Jérôme Picault · David Danan · Mouadh Yagoubi · Benjamin Donnot · Seif Attoui · Pavel Dimitrov · Asma Farjallah · Clement Etienam

Attraction-Repulsion Spectrum in Neighbor Embeddings

Jan Niklas Böhm · Philipp Berens · Dmitry Kobak

Decimated Framelet System on Graphs and Fast G-Framelet Transforms

Xuebin Zheng · Bingxin Zhou · Yuguang Wang · Xiaosheng Zhuang

On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)

Washim Mondal · Mridul Agarwal · Vaneet Aggarwal · Satish Ukkusuri

A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review

Michael Pearce · Elena A. Erosheva

Rethinking Nonlinear Instrumental Variable Models through Prediction Validity

Chunxiao Li · Cynthia Rudin · Tyler H. McCormick

Sufficient reductions in regression with mixed predictors

Efstathia Bura · Liliana Forzani · Rodrigo García Arancibia · Pamela Llop · Diego Tomassi

[Re] An Implementation of Fair Robust Learning

Ian Hardy

[Re] Exacerbating Algorithmic Bias through Fairness Attacks

Matteo Tafuro · Andrea Lombardo · Tin Hadži Veljković · Lasse Becker-Czarnetzki

[Re] Transparent Object Tracking Benchmark

Žiga Trojer

Go to Event Page

Invited Talk

The Forward-Forward Algorithm for Training Deep Neural Networks

Geoffrey Hinton

2:30 PM - 3:30 PM

I will describe a training algorithm for deep neural networks that does not require the neurons to propagate derivatives or remember neural activities. The algorithm can learn multi-level representations of streaming sensory data on the fly without interrupting the processing of the input stream. The algorithm scales much better than reinforcement learning and would be much easier to implement in cortex than backpropagation.

... more

Poster

Poster Session 6

4:00 PM - 6:00 PM

414 Events in this session

ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

Chunyuan Li · Haotian Liu · Liunian Li · Pengchuan Zhang · Jyoti Aneja · Jianwei Yang · Ping Jin · Houdong Hu · Zicheng Liu · Yong Jae Lee · Jianfeng Gao

Ambiguous Images With Human Judgments for Robust Visual Event Classification

Kate Sanders · Reno Kriz · Anqi Liu · Benjamin Van Durme

Recovery and Generalization in Over-Realized Dictionary Learning

Jeremias Sulam · Chong You · Zhihui Zhu

Self-Supervised Learning with an Information Maximization Criterion

Serdar Ozsoy · Shadi Hamdan · Sercan Arik · Deniz Yuret · Alper Erdogan

PAC Prediction Sets for Meta-Learning

Sangdon Park · Edgar Dobriban · Insup Lee · Osbert Bastani

BiT: Robustly Binarized Multi-distilled Transformer

Zechun Liu · Barlas Oguz · Aasish Pappu · Lin Xiao · Scott Yih · Meng Li · Raghuraman Krishnamoorthi · Yashar Mehdad

CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior

Eldar D Abraham · Karel D'Oosterlinck · Amir Feder · Yair Gat · Atticus Geiger · Christopher Potts · Roi Reichart · Zhengxuan Wu

VICRegL: Self-Supervised Learning of Local Visual Features

Adrien Bardes · Jean Ponce · Yann LeCun

GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions

Gene Chou · Ilya Chugunov · Felix Heide

Disentangling Transfer in Continual Reinforcement Learning

Maciej Wolczyk · Michał Zając · Razvan Pascanu · Łukasz Kuciński · Piotr Miłoś

Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees

Siliang Zeng · Chenliang Li · Alfredo Garcia · Mingyi Hong

TUSK: Task-Agnostic Unsupervised Keypoints

Yuhe Jin · Weiwei Sun · Jan Hosang · Eduard Trulls · Kwang Moo Yi

Generalization Gap in Amortized Inference

Mingtian Zhang · Peter Hayes · David Barber

Online Deep Equilibrium Learning for Regularization by Denoising

Jiaming Liu · Xiaojian Xu · Weijie Gan · shirin shoushtari · Ulugbek Kamilov

Deep invariant networks with differentiable augmentation layers

Cédric ROMMEL · Thomas Moreau · Alexandre Gramfort

VICE: Variational Interpretable Concept Embeddings

Lukas Muttenthaler · Charles Zheng · Patrick McClure · Robert Vandermeulen · Martin N Hebart · Francisco Pereira

A contrastive rule for meta-learning

Nicolas Zucchet · Simon Schug · Johannes von Oswald · Dominic Zhao · João Sacramento

Diagnosing failures of fairness transfer across distribution shift in real-world medical settings

Jessica Schrouff · Natalie Harris · Sanmi Koyejo · Ibrahim Alabdulmohsin · Eva Schnider · Krista Opsahl-Ong · Alexander Brown · Subhrajit Roy · Diana Mincu · Christina Chen · Awa Dieng · Yuan Liu · Vivek Natarajan · Alan Karthikesalingam · Katherine Heller · Silvia Chiappa · Alexander D'Amour

A Contrastive Framework for Neural Text Generation

Yixuan Su · Tian Lan · Yan Wang · Dani Yogatama · Lingpeng Kong · Nigel Collier

Redeeming intrinsic rewards via constrained optimization

Eric Chen · Zhang-Wei Hong · Joni Pajarinen · Pulkit Agrawal

NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification

Qitian Wu · Wentao Zhao · Zenan Li · David P Wipf · Junchi Yan

Model Preserving Compression for Neural Networks

Jerry Chee · Megan Flynn (née Renz) · Anil Damle · Christopher De Sa

Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints

Jose Gallego-Posada · Juan Ramirez · Akram Erraqabi · Yoshua Bengio · Simon Lacoste-Julien

Robust Binary Models by Pruning Randomly-initialized Networks

Chen Liu · Ziqi Zhao · Sabine Süsstrunk · Mathieu Salzmann

Training Spiking Neural Networks with Local Tandem Learning

Qu Yang · Jibin Wu · Malu Zhang · Yansong Chua · Xinchao Wang · Haizhou Li

Constrained GPI for Zero-Shot Transfer in Reinforcement Learning

Jaekyeom Kim · Seohong Park · Gunhee Kim

Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization

Long-Kai Huang · Ying Wei

DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems

Ruizhong Qiu · Zhiqing Sun · Yiming Yang

HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences

Siqiao Xue · Xiaoming Shi · James Zhang · Hongyuan Mei

Noise Attention Learning: Enhancing Noise Robustness by Gradient Scaling

Yangdi Lu · Yang Bo · Wenbo He

Self-Aware Personalized Federated Learning

Huili Chen · Jie Ding · Eric W. Tramel · Shuang Wu · Anit Kumar Sahu · Salman Avestimehr · Tao Zhang

Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification

Dandan Guo · Zhuo Li · meixi zheng · He Zhao · Mingyuan Zhou · Hongyuan Zha

Towards Understanding the Mixture-of-Experts Layer in Deep Learning

Zixiang Chen · Yihe Deng · Yue Wu · Quanquan Gu · Yuanzhi Li

Improved Feature Distillation via Projector Ensemble

Yudong Chen · Sen Wang · Jiajun Liu · Xuwei Xu · Frank de Hoog · Zi Huang

Two-Stream Network for Sign Language Recognition and Translation

Yutong Chen · Ronglai Zuo · Fangyun Wei · Yu Wu · Shujie LIU · Brian Mak

Knowledge Distillation from A Stronger Teacher

Tao Huang · Shan You · Fei Wang · Chen Qian · Chang Xu

Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation

Zeyu Qin · Yanbo Fan · Yi Liu · Li Shen · Yong Zhang · Jue Wang · Baoyuan Wu

Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models

Muyang Li · Ji Lin · Chenlin Meng · Stefano Ermon · Song Han · Jun-Yan Zhu

Feature-Proxy Transformer for Few-Shot Segmentation

Jian-Wei Zhang · Yifan Sun · Yi Yang · Wei Chen

Mask Matching Transformer for Few-Shot Segmentation

siyu jiao · Gengwei Zhang · Shant Navasardyan · Ling Chen · Yao Zhao · Yunchao Wei · Honghui Shi

Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection

Shizhen Zhao · Xiaojuan Qi

DaDA: Distortion-aware Domain Adaptation for Unsupervised Semantic Segmentation

Sujin Jang · Joohan Na · Dokwan Oh

AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars

Yue Wu · Yu Deng · Jiaolong Yang · Fangyun Wei · Qifeng Chen · Xin Tong

Selective compression learning of latent representations for variable-rate image compression

Jooyoung Lee · Seyoon Jeong · Munchurl Kim

Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning

Ziyi Zhang · Weikai Chen · Hui Cheng · Zhen Li · Siyuan Li · Liang Lin · Guanbin Li

CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification

Siddhant Kharbanda · Atmadeep Banerjee · Erik Schultheis · Rohit Babbar

Few-Shot Non-Parametric Learning with Deep Latent Variable Model

Zhiying Jiang · Yiqin Dai · Ji Xin · Ming Li · Jimmy Lin

Revisiting Heterophily For Graph Neural Networks

Sitao Luan · Chenqing Hua · Qincheng Lu · Jiaqi Zhu · Mingde Zhao · Shuyuan Zhang · Xiao-Wen Chang · Doina Precup

Distributionally robust weighted k-nearest neighbors

Shixiang Zhu · Liyan Xie · Minghe Zhang · Rui Gao · Yao Xie

ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Zhewei Yao · Reza Yazdani Aminabadi · Minjia Zhang · Xiaoxia Wu · Conglong Li · Yuxiong He

SeqPATE: Differentially Private Text Generation via Knowledge Distillation

Zhiliang Tian · Yingxiu Zhao · Ziyue Huang · Yu-Xiang Wang · Nevin L. Zhang · He He

Inductive Logical Query Answering in Knowledge Graphs

Michael Galkin · Zhaocheng Zhu · Hongyu Ren · Jian Tang

Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks

Zhiwei Deng · Olga Russakovsky

K-LITE: Learning Transferable Visual Models with External Knowledge

Sheng Shen · Chunyuan Li · Xiaowei Hu · Yujia Xie · Jianwei Yang · Pengchuan Zhang · Zhe Gan · Lijuan Wang · Lu Yuan · Ce Liu · Kurt Keutzer · Trevor Darrell · Anna Rohrbach · Jianfeng Gao

Diffusion Visual Counterfactual Explanations

Maximilian Augustin · Valentyn Boreiko · Francesco Croce · Matthias Hein

Graph Neural Networks are Dynamic Programmers

Andrew J Dudzik · Petar Veličković

Assistive Teaching of Motor Control Tasks to Humans

Megha Srivastava · Erdem Biyik · Suvir Mirchandani · Noah Goodman · Dorsa Sadigh

Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off

Mateo Espinosa Zarlenga · Pietro Barbiero · Gabriele Ciravegna · Giuseppe Marra · Francesco Giannini · Michelangelo Diligenti · Zohreh Shams · Frederic Precioso · Stefano Melacci · Adrian Weller · Pietro Lió · Mateja Jamnik

Black-box coreset variational inference

Dionysis Manousakas · Hippolyt Ritter · Theofanis Karaletsos

Supervising the Multi-Fidelity Race of Hyperparameter Configurations

Martin Wistuba · Arlind Kadra · Josif Grabocka

Distributional Reward Estimation for Effective Multi-agent Deep Reinforcement Learning

Jifeng Hu · Yanchao Sun · Hechang Chen · Sili Huang · haiyin piao · Yi Chang · Lichao Sun

Amortized Projection Optimization for Sliced Wasserstein Generative Models

Khai Nguyen · Nhat Ho

CoNSoLe: Convex Neural Symbolic Learning

Haoran Li · Yang Weng · Hanghang Tong

Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity

Mucong Ding · Tahseen Rabbani · Bang An · Evan Wang · Furong Huang

WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting

Youngin Cho · Daejin Kim · DONGMIN KIM · MOHAMMAD AZAM KHAN · Jaegul Choo

Hierarchical Channel-spatial Encoding for Communication-efficient Collaborative Learning

Qihua ZHOU · Song Guo · YI LIU · Jie ZHANG · Jiewei Zhang · Tao GUO · Zhenda XU · Xun Liu · Zhihao Qu

Spatial Pruned Sparse Convolution for Efficient 3D Object Detection

Jianhui Liu · Yukang Chen · Xiaoqing Ye · Zhuotao Tian · Xiao Tan · Xiaojuan Qi

LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery

Chun-Han Yao · Wei-Chih Hung · Yuanzhen Li · Michael Rubinstein · Ming-Hsuan Yang · Varun Jampani

Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data

Armin Thomas · Christopher Ré · Russell Poldrack

Motion Transformer with Global Intention Localization and Local Movement Refinement

Shaoshuai Shi · Li Jiang · Dengxin Dai · Bernt Schiele

Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection

Yiming Li · Yang Bai · Yong Jiang · Yong Yang · Shu-Tao Xia · Bo Li

SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders

Gang Li · Heliang Zheng · Daqing Liu · Chaoyue Wang · Bing Su · Changwen Zheng

Improving Multi-Task Generalization via Regularizing Spurious Correlation

Ziniu Hu · Zhe Zhao · Xinyang Yi · Tiansheng Yao · Lichan Hong · Yizhou Sun · Ed Chi

Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels

Ming-Kun Xie · Jiahao Xiao · Sheng-Jun Huang

One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations

Yiming Zhu · Hongyu Liu · Yibing Song · Ziyang Yuan · Xintong Han · Chun Yuan · Qifeng Chen · Jue Wang

Latency-aware Spatial-wise Dynamic Networks

Yizeng Han · Zhihang Yuan · Yifan Pu · Chenhao Xue · Shiji Song · Guangyu Sun · Gao Huang

Why Do Artificially Generated Data Help Adversarial Robustness

Yue Xing · Qifan Song · Guang Cheng

Pre-activation Distributions Expose Backdoor Neurons

Runkai Zheng · Rongjun Tang · Jianze Li · Li Liu

Recovering Private Text in Federated Learning of Language Models

Samyak Gupta · Yangsibo Huang · Zexuan Zhong · Tianyu Gao · Kai Li · Danqi Chen

Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks

Minji Yoon · John Palowitch · Dustin Zelle · Ziniu Hu · Ruslan Salakhutdinov · Bryan Perozzi

Towards Robust Blind Face Restoration with Codebook Lookup Transformer

Shangchen Zhou · Kelvin Chan · Chongyi Li · Chen Change Loy

S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning

Daesol Cho · Dongseok Shim · H. Jin Kim

NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation

Jiaqi Gu · Zhengqi Gao · Chenghao Feng · Hanqing Zhu · Ray Chen · Duane Boning · David Pan

UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units

Igor Fedorov · Ramon Matas · Hokchhay Tann · Chuteng Zhou · Matthew Mattina · Paul Whatmough

Using Embeddings for Causal Estimation of Peer Influence in Social Networks

Irina Cristali · Victor Veitch

Batch size-invariance for policy optimization

Jacob Hilton · Karl Cobbe · John Schulman

Top Two Algorithms Revisited

Marc Jourdan · Rémy Degenne · Dorian Baudry · Rianne de Heide · Emilie Kaufmann

Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs

Cristian Bodnar · Francesco Di Giovanni · Benjamin Chamberlain · Pietro Lió · Michael Bronstein

Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources

Bariscan Bozkurt · Cengiz Pehlevan · Alper Erdogan

When does return-conditioned supervised learning work for offline reinforcement learning?

David Brandfonbrener · Alberto Bietti · Jacob Buckman · Romain Laroche · Joan Bruna

Communication Efficient Distributed Learning for Kernelized Contextual Bandits

Chuanhao Li · Huazheng Wang · Mengdi Wang · Hongning Wang

TaSIL: Taylor Series Imitation Learning

Daniel Pfrommer · Thomas Zhang · Stephen Tu · Nikolai Matni

DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization

Kevin Bello · Bryon Aragam · Pradeep Ravikumar

Efficient Active Learning with Abstention

Yinglun Zhu · Robert Nowak

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

Yu-Guan Hsieh · Kimon Antonakopoulos · Volkan Cevher · Panayotis Mertikopoulos

Integral Probability Metrics PAC-Bayes Bounds

Ron Amit · Baruch Epstein · Shay Moran · Ron Meir

A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data

Jelena Diakonikolas · Chenghui Li · Swati Padmanabhan · Chaobing Song

Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications

Daniel Lee · Georgy Noarov · Mallesh Pai · Aaron Roth

Nonstationary Dual Averaging and Online Fair Allocation

Luofeng Liao · Yuan Gao · Christian Kroer

The price of unfairness in linear bandits with biased feedback

Solenne Gaucher · Alexandra Carpentier · Christophe Giraud

Augmenting Online Algorithms with $\varepsilon$-Accurate Predictions

Anupam Gupta · Debmalya Panigrahi · Bernardo Subercaseaux · Kevin Sun

A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models

Lijia Zhou · Frederic Koehler · Pragya Sur · Danica J. Sutherland · Nati Srebro

Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime

Benjamin Bowman · Guido Montufar

Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data

Yusuke Tanaka · Tomoharu Iwata · naonori ueda

Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks

Ching-Yao Chuang · Stefanie Jegelka

On the Parameterization and Initialization of Diagonal State Space Models

Albert Gu · Karan Goel · Ankit Gupta · Christopher Ré

On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs

Arjun Subramonian · Kai-Wei Chang · Yizhou Sun

Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge

Mayleen Cortez · Matthew Eichhorn · Christina Yu

Near-Optimal Multi-Agent Learning for Safe Coverage Control

Manish Prajapat · Matteo Turchetta · Melanie Zeilinger · Andreas Krause

Deep Counterfactual Estimation with Categorical Background Variables

Edward De Brouwer

Using Partial Monotonicity in Submodular Maximization

Loay Mualem · Moran Feldman

A New Family of Generalization Bounds Using Samplewise Evaluated CMI

Fredrik Hellström · Giuseppe Durisi

Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes

Alessio Mazzetto · Cristina Menghini · Andrew Yuan · Eli Upfal · Stephen Bach

Bellman Residual Orthogonalization for Offline Reinforcement Learning

Andrea Zanette · Martin J Wainwright

Riemannian Diffusion Models

Chin-Wei Huang · Milad Aghajohari · Joey Bose · Prakash Panangaden · Aaron Courville

Formulating Robustness Against Unforeseen Attacks

Sihui Dai · Saeed Mahloujifar · Prateek Mittal

Characterizing Datapoints via Second-Split Forgetting

Pratyush Maini · Saurabh Garg · Zachary Lipton · J. Zico Kolter

Fairness Transferability Subject to Bounded Distribution Shift

Yatong Chen · Reilly Raab · Jialu Wang · Yang Liu

Oracle Inequalities for Model Selection in Offline Reinforcement Learning

Jonathan N Lee · George Tucker · Ofir Nachum · Bo Dai · Emma Brunskill

Simulation-guided Beam Search for Neural Combinatorial Optimization

Jinho Choo · Yeong-Dae Kwon · Jihoon Kim · Jeongwoo Jae · André Hottung · Kevin Tierney · Youngjune Gwon

Stars: Tera-Scale Graph Building for Clustering and Learning

CJ Carey · Jonathan Halcrow · Rajesh Jayaram · Vahab Mirrokni · Warren Schudy · Peilin Zhong

Estimating and Explaining Model Performance When Both Covariates and Labels Shift

Lingjiao Chen · Matei Zaharia · James Zou

Simultaneous Missing Value Imputation and Structure Learning with Groups

Pablo Morales-Alvarez · Wenbo Gong · Angus Lamb · Simon Woodhead · Simon Peyton Jones · Nick Pawlowski · Miltiadis Allamanis · Cheng Zhang

Signal Processing for Implicit Neural Representations

Dejia Xu · Peihao Wang · Yifan Jiang · Zhiwen Fan · Zhangyang Wang

Regularized Molecular Conformation Fields

Lihao Wang · Yi Zhou · Yiqun Wang · Xiaoqing Zheng · Xuanjing Huang · Hao Zhou

GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations

Enmao Diao · Jie Ding · Vahid Tarokh

When Does Group Invariant Learning Survive Spurious Correlations?

Yimeng Chen · Ruibin Xiong · Zhi-Ming Ma · Yanyan Lan

Diverse Weight Averaging for Out-of-Distribution Generalization

Alexandre Rame · Matthieu Kirchmeyer · Thibaud Rahier · Alain Rakotomamonjy · Patrick Gallinari · Matthieu Cord

KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation

Ta-Chung Chi · Ting-Han Fan · Peter J Ramadge · Alexander Rudnicky

Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome

Elliott Gordon-Rodriguez · Thomas Quinn · John Cunningham

Improving Certified Robustness via Statistical Learning with Logical Reasoning

Zhuolin Yang · Zhikuan Zhao · Boxin Wang · Jiawei Zhang · Linyi Li · Hengzhi Pei · Bojan Karlaš · Ji Liu · Heng Guo · Ce Zhang · Bo Li

Cooperative Distribution Alignment via JSD Upper Bound

Wonwoong Cho · ZIYU GONG · David Inouye

Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization

Yibo Zeng · Henry Lam

Minimax Optimal Fixed-Budget Best Arm Identification in Linear Bandits

Junwen Yang · Vincent Tan

Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients

Kyurae Kim · Jisu Oh · Jacob Gardner · Adji Bousso Dieng · Hongseok Kim

A Kernelised Stein Statistic for Assessing Implicit Generative Models

Wenkai Xu · Gesine D Reinert

Effects of Data Geometry in Early Deep Learning

Saket Tiwari · George Konidaris

Deep Differentiable Logic Gate Networks

Felix Petersen · Christian Borgelt · Hilde Kuehne · Oliver Deussen

Exploring the Latent Space of Autoencoders with Interventional Assays

Felix Leeb · Stefan Bauer · Michel Besserve · Bernhard Schölkopf

Semi-Discrete Normalizing Flows through Differentiable Tessellation

Ricky T. Q. Chen · Brandon Amos · Maximilian Nickel

Video Diffusion Models

Jonathan Ho · Tim Salimans · Alexey Gritsenko · William Chan · Mohammad Norouzi · David Fleet

What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs

Tal Shaharabany · Yoad Tewel · Lior Wolf

Generative multitask learning mitigates target-causing confounding

Taro Makino · Krzysztof Geras · Kyunghyun Cho

Adversarial Task Up-sampling for Meta-learning

Yichen WU · Long-Kai Huang · Ying Wei

DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning

Seungjae Lee · Jigang Kim · Inkyu Jang · H. Jin Kim

Entropy-Driven Mixed-Precision Quantization for Deep Network Design

Zhenhong Sun · Ce Ge · Junyan Wang · Ming Lin · Hesen Chen · Hao Li · Xiuyu Sun

Efficient and Effective Augmentation Strategy for Adversarial Training

Sravanti Addepalli · Samyak Jain · Venkatesh Babu R

Dataset Inference for Self-Supervised Models

Adam Dziedzic · Haonan Duan · Muhammad Ahmad Kaleem · Nikita Dhawan · Jonas Guan · Yannis Cattan · Franziska Boenisch · Nicolas Papernot

Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training

Peng Jiang · Lihan Hu · Shihui Song

Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?

Yibo Yang · Shixiang Chen · Xiangtai Li · Liang Xie · Zhouchen Lin · Dacheng Tao

Alleviating Adversarial Attacks on Variational Autoencoders with MCMC

Anna Kuzina · Max Welling · Jakub Tomczak

Smoothed Embeddings for Certified Few-Shot Learning

Mikhail Pautov · Olesya Kuznetsova · Nurislam Tursynbek · Aleksandr Petiushko · Ivan Oseledets

Instability and Local Minima in GAN Training with Kernel Discriminators

Evan Becker · Parthe Pandit · Sundeep Rangan · Alyson Fletcher

Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions

Audrey Huang · Nan Jiang

On Kernelized Multi-Armed Bandits with Constraints

Xingyu Zhou · Bo Ji

Target alignment in truncated kernel ridge regression

Arash Amini · Richard Baumgartner · Dai Feng

Local Bayesian optimization via maximizing probability of descent

Quan Nguyen · Kaiwen Wu · Jacob Gardner · Roman Garnett

MEMO: Test Time Robustness via Adaptation and Augmentation

Marvin Zhang · Sergey Levine · Chelsea Finn

Latent Planning via Expansive Tree Search

Robert Gieselmann · Florian T. Pokorny

Efficient and Effective Optimal Transport-Based Biclustering

Chakib Fettal · lazhar labiod · Mohamed NADIF

Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks

Michael Lohaus · Matthäus Kleindessner · Krishnaram Kenthapadi · Francesco Locatello · Chris Russell

On the Adversarial Robustness of Mixture of Experts

Joan Puigcerver · Rodolphe Jenatton · Carlos Riquelme · Pranjal Awasthi · Srinadh Bhojanapalli

Scalable Distributional Robustness in a Class of Non-Convex Optimization with Guarantees

Avinandan Bose · Arunesh Sinha · Tien Mai

A framework for bilevel optimization that enables stochastic and global variance reduction algorithms

Mathieu Dagréou · Pierre Ablin · Samuel Vaiter · Thomas Moreau

Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions

Antonio Terpin · Nicolas Lanzetti · Batuhan Yardim · Florian Dorfler · Giorgia Ramponi

LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation

Geon-Hyeong Kim · Jongmin Lee · Youngsoo Jang · Hongseok Yang · Kee-Eung Kim

A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization

Songtao Lu · Siliang Zeng · Xiaodong Cui · Mark Squillante · Lior Horesh · Brian Kingsbury · Jia Liu · Mingyi Hong

Amortized Proximal Optimization

Juhan Bae · Paul Vicol · Jeff Z. HaoChen · Roger Grosse

Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel

Changyong Oh · Roberto Bondesan · Efstratios Gavves · Max Welling

On the difficulty of learning chaotic dynamics with RNNs

Jonas Mikhaeil · Zahra Monfared · Daniel Durstewitz

Learning to Configure Computer Networks with Neural Algorithmic Reasoning

Luca Beurer-Kellner · Martin Vechev · Laurent Vanbever · Petar Veličković

Autoregressive Search Engines: Generating Substrings as Document Identifiers

Michele Bevilacqua · Giuseppe Ottaviano · Patrick Lewis · Scott Yih · Sebastian Riedel · Fabio Petroni

Efficient learning of nonlinear prediction models with time-series privileged information

Bastian Jung · Fredrik Johansson

Revisiting Active Sets for Gaussian Process Decoders

Pablo Moreno-Muñoz · Cilie Feldager · Søren Hauberg

Tractable Function-Space Variational Inference in Bayesian Neural Networks

Tim G. J. Rudner · Zonghao Chen · Yee Whye Teh · Yarin Gal

RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks

Leo Kozachkov · Michaela Ennis · Jean-Jacques Slotine

Use-Case-Grounded Simulations for Explanation Evaluation

Valerie Chen · Nari Johnson · Nicholay Topin · Gregory Plumb · Ameet Talwalkar

Autoformalization with Large Language Models

Yuhuai Wu · Albert Qiaochu Jiang · Wenda Li · Markus Rabe · Charles Staats · Mateja Jamnik · Christian Szegedy

VCT: A Video Compression Transformer

Fabian Mentzer · George D Toderici · David Minnen · Sergi Caelles · Sung Jin Hwang · Mario Lucic · Eirikur Agustsson

Diffusion Curvature for Estimating Local Curvature in High Dimensional Data

Dhananjay Bhaskar · Kincaid MacDonald · Oluwadamilola Fasina · Dawson Thomas · Bastian Rieck · Ian Adelstein · Smita Krishnaswamy

Linear tree shap

peng yu · Albert Bifet · Jesse Read · Chao Xu

Kernel Interpolation with Sparse Grids

Mohit Yadav · Daniel Sheldon · Cameron Musco

Adaptive Interest for Emphatic Reinforcement Learning

Martin Klissarov · Rasool Fakoor · Jonas Mueller · Kavosh Asadi · Taesup Kim · Alexander Smola

Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems

Masatoshi Uehara · Ayush Sekhari · Jason Lee · Nathan Kallus · Wen Sun

Towards Improving Calibration in Object Detection Under Domain Shift

Muhammad Akhtar Munir · Muhammad Haris Khan · M. Sarfraz · Mohsen Ali

ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward

Zixian Ma · Rose Wang · Fei-Fei Li · Michael Bernstein · Ranjay Krishna

Model-Based Imitation Learning for Urban Driving

Anthony Hu · Gianluca Corrado · Nicolas Griffiths · Zachary Murez · Corina Gurau · Hudson Yeo · Alex Kendall · Roberto Cipolla · Jamie Shotton

Repairing Neural Networks by Leaving the Right Past Behind

Ryutaro Tanno · Melanie F. Pradier · Aditya Nori · Yingzhen Li

Meta-Reinforcement Learning with Self-Modifying Networks

Mathieu Chalvidal · Thomas Serre · Rufin VanRullen

The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning

Xi Ye · Greg Durrett

NeuForm: Adaptive Overfitting for Neural Shape Editing

Connor Lin · Niloy Mitra · Gordon Wetzstein · Leonidas Guibas · Paul Guerrero

Semantic uncertainty intervals for disentangled latent spaces

Swami Sankaranarayanan · Anastasios Angelopoulos · Stephen Bates · Yaniv Romano · Phillip Isola

Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces

Leonard Papenmeier · Luigi Nardi · Matthias Poloczek

First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data

Mao Ye · Lemeng Wu · Qiang Liu

On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond

Xiaotong Yuan · Ping Li

Multi-Objective Deep Learning with Adaptive Reference Vectors

Weiyu Chen · James Kwok

The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning

Yunhao Tang · Remi Munos · Mark Rowland · Bernardo Avila Pires · Will Dabney · Marc Bellemare

Envy-free Policy Teaching to Multiple Agents

Jiarui Gan · R Majumdar · Adish Singla · Goran Radanovic

When are Local Queries Useful for Robust Learning?

Pascale Gourdeau · Varun Kanade · Marta Kwiatkowska · James Worrell

Generalization for multiclass classification with overparameterized linear models

Vignesh Subramanian · Rahul Arya · Anant Sahai

Statistical Learning and Inverse Problems: A Stochastic Gradient Approach

Yuri Fonseca · Yuri Saporito

Algorithms that Approximate Data Removal: New Results and Limitations

Vinith Suriyakumar · Ashia Wilson

Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms

Xutong Liu · Jinhang Zuo · Siwei Wang · Carlee Joe-Wong · John C.S. Lui · Wei Chen

Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent

Yu Bai · Chi Jin · Song Mei · Ziang Song · Tiancheng Yu

Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space

Jonatha Anselmi · Bruno Gaujal · Louis-Sébastien Rebuffi

Provably Efficient Model-Free Constrained RL with Linear Function Approximation

Arnob Ghosh · Xingyu Zhou · Ness Shroff

Convergence for score-based generative modeling with polynomial complexity

Holden Lee · Jianfeng Lu · Yixin Tan

Efficient Dataset Distillation using Random Feature Approximation

Noel Loo · Ramin Hasani · Alexander Amini · Daniela Rus

The Effects of Regularization and Data Augmentation are Class Dependent

Randall Balestriero · Leon Bottou · Yann LeCun

EpiGRAF: Rethinking training of 3D GANs

Ivan Skorokhodov · Sergey Tulyakov · Yiqun Wang · Peter Wonka

Insights into Pre-training via Simpler Synthetic Tasks

Yuhuai Wu · Felix Li · Percy Liang

GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Tim Dettmers · Mike Lewis · Younes Belkada · Luke Zettlemoyer

STNDT: Modeling Neural Population Activity with Spatiotemporal Transformers

Trung Le · Eli Shlizerman

Neural Shape Deformation Priors

Jiapeng Tang · Lev Markhasin · Bi Wang · Justus Thies · Matthias Niessner

SPoVT: Semantic-Prototype Variational Transformer for Dense Point Cloud Semantic Completion

Sheng Yu Huang · Hao-Yu Hsu · Frank Wang

Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone

Zi-Yi Dou · Aishwarya Kamath · Zhe Gan · Pengchuan Zhang · Jianfeng Wang · Linjie Li · Zicheng Liu · Ce Liu · Yann LeCun · Nanyun Peng · Jianfeng Gao · Lijuan Wang

Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer

Sen Lin · Li Yang · Deliang Fan · Junshan Zhang

SKFlow: Learning Optical Flow with Super Kernels

SHANGKUN SUN · Yuanqi Chen · Yu Zhu · Guodong Guo · Ge Li

Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks

Yen-Cheng Liu · CHIH-YAO MA · Junjiao Tian · Zijian He · Zsolt Kira

Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative

Tianxin Wei · Yuning You · Tianlong Chen · Yang Shen · Jingrui He · Zhangyang Wang

3D Concept Grounding on Neural Fields

Yining Hong · Yilun Du · Chunru Lin · Josh Tenenbaum · Chuang Gan

Bridge the Gap Between Architecture Spaces via A Cross-Domain Predictor

Yuqiao Liu · Yehui Tang · Zeqiong Lv · Yunhe Wang · Yanan Sun

Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation

YUANWEI LIU · Nian Liu · Xiwen Yao · Junwei Han

Uncertainty-Aware Hierarchical Refinement for Incremental Implicitly-Refined Classification

Jian Yang · Kai Zhu · Kecheng Zheng · Yang Cao

PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds

Aoran Xiao · Jiaxing Huang · Dayan Guan · Kaiwen Cui · Shijian Lu · Ling Shao

ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model

Rao Fu · Xiao Zhan · YIWEN CHEN · Daniel Ritchie · Srinath Sridhar

On the Learning Mechanisms in Physical Reasoning

Shiqian Li · Kewen Wu · Chi Zhang · Yixin Zhu

Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting

Neil Mallinar · James Simon · Amirhesam Abedsoltan · Parthe Pandit · Misha Belkin · Preetum Nakkiran

The trade-offs of model size in large recommendation models : 100GB to 10MB Criteo-tb DLRM model

Aditya Desai · Anshumali Shrivastava

Geodesic Graph Neural Network for Efficient Graph Representation Learning

Lecheng Kong · Yixin Chen · Muhan Zhang

Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited

Mingguo He · Zhewei Wei · Ji-Rong Wen

SignRFF: Sign Random Fourier Features

Xiaoyun Li · Ping Li

Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems

Hyunwoong Chang · Changwoo Lee · Zhao Tang Luo · Huiyan Sang · Quan Zhou

What is a Good Metric to Study Generalization of Minimax Learners?

Asuman Ozdaglar · Sarath Pattathil · Jiawei Zhang · Kaiqing Zhang

Non-stationary Bandits with Knapsacks

Shang Liu · Jiashuo Jiang · Xiaocheng Li

Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization

Arman Zharmagambetov · Miguel A. Carreira-Perpinan

Learning on Arbitrary Graph Topologies via Predictive Coding

Tommaso Salvatori · Luca Pinchetti · Beren Millidge · Yuhang Song · Tianyi Bao · Rafal Bogacz · Thomas Lukasiewicz

Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning

Yuchen Xiao · Weihao Tan · Christopher Amato

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

Bowen Baker · Ilge Akkaya · Peter Zhokov · Joost Huizinga · Jie Tang · Adrien Ecoffet · Brandon Houghton · Raul Sampedro · Jeff Clune

Patching open-vocabulary models by interpolating weights

Gabriel Ilharco · Mitchell Wortsman · Samir Yitzhak Gadre · Shuran Song · Hannaneh Hajishirzi · Simon Kornblith · Ali Farhadi · Ludwig Schmidt

Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models

Chen Henry Wu · Saman Motamed · Shaunak Srivastava · Fernando D De la Torre

Learning Options via Compression

Yiding Jiang · Evan Liu · Benjamin Eysenbach · J. Zico Kolter · Chelsea Finn

On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning

Mandi Zhao · Pieter Abbeel · Stephen James

Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress

Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare

Rethinking Generalization in Few-Shot Classification

Markus Hiller · Rongkai Ma · Mehrtash Harandi · Tom Drummond

ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model

Srishti Gautam · Ahcène Boubekki · Stine Hansen · Suaiba Salahuddin · Robert Jenssen · Marina Höhne · Michael Kampffmeyer

Deep Fourier Up-Sampling

man zhou · Hu Yu · Jie Huang · Feng Zhao · Jinwei Gu · Chen Change Loy · Deyu Meng · Chongyi Li

Provable Subspace Identification Under Post-Nonlinear Mixtures

Qi Lyu · Xiao Fu

Distributed Distributionally Robust Optimization with Non-Convex Objectives

Yang Jiao · Kai Yang · Dongjin Song

GhostNetV2: Enhance Cheap Operation with Long-Range Attention

Yehui Tang · Kai Han · Jianyuan Guo · Chang Xu · Chao Xu · Yunhe Wang

Exploiting Semantic Relations for Glass Surface Detection

Jiaying Lin · Yuen-Hei Yeung · Rynson Lau

A Simple Approach to Automated Spectral Clustering

Jicong Fan · Yiheng Tu · Zhao Zhang · Mingbo Zhao · Haijun Zhang

Hiding Images in Deep Probabilistic Models

Haoyu Chen · Linqi Song · Zhenxing Qian · Xinpeng Zhang · Kede Ma

Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective

Keitaro Sakamoto · Issei Sato

Large Language Models are Zero-Shot Reasoners

Takeshi Kojima · Shixiang (Shane) Gu · Machel Reid · Yutaka Matsuo · Yusuke Iwasawa

Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization

Minsu Kim · Junyoung Park · Jinkyoo Park

Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets

Ruisi Cai · Zhenyu Zhang · Tianlong Chen · Xiaohan Chen · Zhangyang Wang

Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning

Dongmin Park · Yooju Shin · Jihwan Bang · Youngjun Lee · Hwanjun Song · Jae-Gil Lee

A Simple Decentralized Cross-Entropy Method

Zichen Zhang · Jun Jin · Martin Jagersand · Jun Luo · Dale Schuurmans

The Missing Invariance Principle found -- the Reciprocal Twin of Invariant Risk Minimization

Dongsung Huh · Avinash Baidya

Active Learning with Safety Constraints

Romain Camilleri · Andrew Wagenmaker · Jamie Morgenstern · Lalit Jain · Kevin Jamieson

A PAC-Bayesian Generalization Bound for Equivariant Networks

Arash Behboodi · Gabriele Cesa · Taco Cohen

When to Intervene: Learning Optimal Intervention Policies for Critical Events

Niranjan Damera Venkata · Chiranjib Bhattacharyya

Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm

Aidan Good · Jiaqi Lin · Xin Yu · Hannah Sieg · Mikey Fergurson · Shandian Zhe · Jerzy Wieczorek · Thiago Serra

Efficient and Modular Implicit Differentiation

Mathieu Blondel · Quentin Berthet · Marco Cuturi · Roy Frostig · Stephan Hoyer · Felipe Llinares-Lopez · Fabian Pedregosa · Jean-Philippe Vert

Detection and Localization of Changes in Conditional Distributions

Lizhen Nie · Dan Nicolae

Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers

Luke Marris · Ian Gemp · Thomas Anthony · Andrea Tacchetti · Siqi Liu · Karl Tuyls

DGD^2: A Linearly Convergent Distributed Algorithm For High-dimensional Statistical Recovery

Marie Maros · Gesualdo Scutari

Robust Streaming PCA

Daniel Bienstock · Minchan Jeong · Apurv Shukla · Se-Young Yun

Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model

Pranjal Awasthi · Abhimanyu Das · Weihao Kong · Rajat Sen

Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space

Yunbum Kook · Yin-Tat Lee · Ruoqi Shen · Santosh Vempala

Off-Policy Evaluation with Policy-Dependent Optimization Response

Wenshuo Guo · Michael Jordan · Angela Zhou

Bayesian Risk Markov Decision Processes

Yifan Lin · Yuxuan Ren · Enlu Zhou

Variational inference via Wasserstein gradient flows

Marc Lambert · Sinho Chewi · Francis Bach · Silvère Bonnabel · Philippe Rigollet

Alternating Mirror Descent for Constrained Min-Max Games

Andre Wibisono · Molei Tao · Georgios Piliouras

Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions

Adam Block · Max Simchowitz

Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence

Boyi Liu · Jiayang Li · Zhuoran Yang · Hoi-To Wai · Mingyi Hong · Yu Nie · Zhaoran Wang

Mean Estimation with User-level Privacy under Data Heterogeneity

Rachel Cummings · Vitaly Feldman · Audra McMillan · Kunal Talwar

Intrinsic dimensionality estimation using Normalizing Flows

Christian Horvat · Jean-Pascal Pfister

Washing The Unwashable : On The (Im)possibility of Fairwashing Detection

Ali Shahin Shamsabadi · Mohammad Yaghini · Natalie Dullerud · Sierra Wyllie · Ulrich Aïvodji · Aisha Alaagib · Sébastien Gambs · Nicolas Papernot

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank

Alessandro Epasto · Vahab Mirrokni · Bryan Perozzi · Anton Tsitsulin · Peilin Zhong

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models

Haoyue Dai · Peter Spirtes · Kun Zhang

Explainable Reinforcement Learning via Model Transforms

Mira Finkelstein · Nitsan levy · Lucy Liu · Yoav Kolumbus · David Parkes · Jeffrey S Rosenschein · Sarah Keren

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability

Roman Levin · Manli Shu · Eitan Borgnia · Furong Huang · Micah Goldblum · Tom Goldstein

Neural Circuit Architectural Priors for Embodied Control

Nikhil Bhattasali · Anthony M Zador · Tatiana Engel

Action-modulated midbrain dopamine activity arises from distributed control policies

Jack Lindsey · Ashok Litwin-Kumar

When are Offline Two-Player Zero-Sum Markov Games Solvable?

Qiwen Cui · Simon Du

Private and Communication-Efficient Algorithms for Entropy Estimation

Gecia Bravo-Hermsdorff · Róbert Busa-Fekete · Mohammad Ghavamzadeh · Andres Munoz Medina · Umar Syed

Approximate Value Equivalence

Christopher Grimm · Andre Barreto · Satinder Singh

Size and depth of monotone neural networks: interpolation and approximation

Dan Mikulincer · Daniel Reichman

Characterization of Excess Risk for Locally Strongly Convex Population Risk

Mingyang Yi · Ruoyu Wang · Zhi-Ming Ma

Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs

Dongruo Zhou · Quanquan Gu

A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs

Chloé Rouyer · Dirk van der Hoeven · Nicolò Cesa-Bianchi · Yevgeny Seldin

Optimal Rates for Regularized Conditional Mean Embedding Learning

Zhu Li · Dimitri Meunier · Mattes Mollenhauer · Arthur Gretton

The Burer-Monteiro SDP method can fail even above the Barvinok-Pataki bound

Liam O'Carroll · Vaidehi Srinivas · Aravindan Vijayaraghavan

Contextual Dynamic Pricing with Unknown Noise: Explore-then-UCB Strategy and Improved Regrets

Yiyun Luo · Will Wei Sun · Yufeng Liu

Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems

Yue Kang · Cho-Jui Hsieh · Thomas Chun Man Lee

Generic bounds on the approximation error for physics-informed (and) operator learning

Tim De Ryck · Siddhartha Mishra

Zero-Sum Stochastic Stackelberg Games

Denizalp Goktas · Sadie Zhao · Amy Greenwald

Fair and Efficient Allocations Without Obvious Manipulations

Alexandros Psomas · Paritosh Verma

Faster Linear Algebra for Distance Matrices

Piotr Indyk · Sandeep Silwal

SQ Lower Bounds for Learning Single Neurons with Massart Noise

Ilias Diakonikolas · Daniel Kane · Lisheng Ren · Yuxin Sun

A Differentially Private Linear-Time fPTAS for the Minimum Enclosing Ball Problem

Bar Mahpud · Or Sheffet

Subquadratic Kronecker Regression with Applications to Tensor Decomposition

Matthew Fahrbach · Gang Fu · Mehrdad Ghadiri

Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity

Dmitry Kovalev · Aleksandr Beznosikov · Ekaterina Borodich · Alexander Gasnikov · Gesualdo Scutari

Adam Can Converge Without Any Modification On Update Rules

Yushun Zhang · Congliang Chen · Naichen Shi · Ruoyu Sun · Zhi-Quan Luo

Oracle-Efficient Online Learning for Smoothed Adversaries

Nika Haghtalab · Yanjun Han · Abhishek Shetty · Kunhe Yang

Active Learning of Classifiers with Label and Seed Queries

Marco Bressan · Nicolò Cesa-Bianchi · Silvio Lattanzi · Andrea Paudice · Maximilian Thiessen

Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments

Liyu Chen · Haipeng Luo

Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning

Anastasiia Koloskova · Sebastian Stich · Martin Jaggi

Improved Coresets for Euclidean $k$-Means

Vincent Cohen-Addad · Kasper Green Larsen · David Saulpic · Chris Schwiegelshohn · Omar Ali Sheikh-Omar

Sampling from Log-Concave Distributions with Infinity-Distance Guarantees

Oren Mangoubi · Nisheeth Vishnoi

Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions

Xufeng Cai · Chaobing Song · Cristóbal Guzmán · Jelena Diakonikolas

Sequential Information Design: Learning to Persuade in the Dark

Martino Bernasconi · Matteo Castiglioni · Alberto Marchesi · Nicola Gatti · Francesco Trovò

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

Ali Kavis · Stratis Skoulakis · Kimon Antonakopoulos · Leello Tadesse Dadi · Volkan Cevher

Exponential Separations in Symmetric Neural Networks

Aaron Zweig · Joan Bruna

Distributional Convergence of the Sliced Wasserstein Process

Jiaqi Xi · Jonathan Niles-Weed

Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms

Alireza Fallah · Ali Makhdoumi · azarakhsh malekian · Asuman Ozdaglar

Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes

Joachim Rubinstein · Benjamin Rubinstein

On Margin Maximization in Linear and ReLU Networks

Gal Vardi · Ohad Shamir · Nati Srebro

Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization

Simone Bombari · Mohammad Hossein Amani · Marco Mondelli

A Scalable Deterministic Global Optimization Algorithm for Training Optimal Decision Tree

Kaixun Hua · Jiayang Ren · Yankai Cao

Is $L^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network?

Chuwei Wang · Shanda Li · Di He · Liwei Wang

Recursive Reasoning in Minimax Games: A Level $k$ Gradient Play Method

Zichu Liu · Lacra Pavel

Concept Activation Regions: A Generalized Framework For Concept-Based Explanations

Jonathan Crabbé · Mihaela van der Schaar

Fair Rank Aggregation

Diptarka Chakraborty · Syamantak Das · Arindam Khan · Aditya Subramanian

Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning

Grigory Malinovsky · Kai Yi · Peter Richtarik

Recursive Reinforcement Learning

Ernst Moritz Hahn · Mateo Perez · Sven Schewe · Fabio Somenzi · Ashutosh Trivedi · Dominik Wojtczak

The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?

Jean Barbier · TianQi Hou · Marco Mondelli · Manuel Saenz

PDSketch: Integrated Domain Programming, Learning, and Planning

Jiayuan Mao · Tomás Lozano-Pérez · Josh Tenenbaum · Leslie Kaelbling

Human-AI Shared Control via Policy Dissection

Quanyi Li · Zhenghao Peng · Haibin Wu · Lan Feng · Bolei Zhou

Embodied Scene-aware Human Pose Estimation

Zhengyi Luo · Shun Iwase · Ye Yuan · Kris Kitani

A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization

Puyuan Liu · Xiang Zhang · Lili Mou

E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance

Can Chang · Ni Mu · Jiajun Wu · Ling Pan · Huazhe Xu

Semi-Supervised Generative Models for Multiagent Trajectories

Dennis Fassmeyer · Pascal Fassmeyer · Ulf Brefeld

Scale-invariant Learning by Physics Inversion

Philipp Holl · Vladlen Koltun · Nils Thuerey

Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language

Zhenlin Xu · Marc Niethammer · Colin Raffel

Are Defenses for Graph Neural Networks Robust?

Felix Mujkanovic · Simon Geisler · Stephan Günnemann · Aleksandar Bojchevski

Human-AI Collaborative Bayesian Optimisation

Arun Kumar A V · Santu Rana · Alistair Shilton · Svetha Venkatesh

On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity

Vincent Szolnoky · Viktor Andersson · Balazs Kulcsar · Rebecka Jörnsten

Modular Flows: Differential Molecular Generation

Yogesh Verma · Samuel Kaski · Markus Heinonen · Vikas Garg

Error Correction Code Transformer

Yoni Choukroun · Lior Wolf

BYOL-Explore: Exploration by Bootstrapped Prediction

Zhaohan Guo · Shantanu Thakoor · Miruna Pislar · Bernardo Avila Pires · Florent Altché · Corentin Tallec · Alaa Saade · Daniele Calandriello · Jean-Bastien Grill · Yunhao Tang · Michal Valko · Remi Munos · Mohammad Gheshlaghi Azar · Bilal Piot

TransBoost: Improving the Best ImageNet Performance using Deep Transduction

Omer Belhasin · Guy Bar-Shalom · Ran El-Yaniv

MorphTE: Injecting Morphology in Tensorized Embeddings

Guobing Gan · Peng Zhang · Sunzhu Li · Xiuqing Lu · Benyou Wang

Attention-based Neural Cellular Automata

Mattie Tesfaldet · Derek Nowrouzezahrai · Chris Pal

Benchopt: Reproducible, efficient and collaborative optimization benchmarks

Thomas Moreau · Mathurin Massias · Alexandre Gramfort · Pierre Ablin · Pierre-Antoine Bannier · Benjamin Charlier · Mathieu Dagréou · Tom Dupre la Tour · Ghislain DURIF · Cassio F. Dantas · Quentin Klopfenstein · Johan Larsson · En Lai · Tanguy Lefort · Benoît Malézieux · Badr MOUFAD · Binh T. Nguyen · Alain Rakotomamonjy · Zaccharie Ramzi · Joseph Salmon · Samuel Vaiter

EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL

Thomas Carta · Pierre-Yves Oudeyer · Olivier Sigaud · Sylvain Lamprier

VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement

Erik Wijmans · Irfan Essa · Dhruv Batra

Redistribution of Weights and Activations for AdderNet Quantization

Ying Nie · Kai Han · Haikang Diao · Chuanjian Liu · Enhua Wu · Yunhe Wang

Eliciting Thinking Hierarchy without a Prior

Yuqing Kong · Yunqi Li · Yubo Zhang · Zhihuan Huang · Jinzhao Wu

First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization

Siddharth Reddy · Sergey Levine · Anca Dragan

Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials

Eshaan Nichani · Yu Bai · Jason Lee

Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy

Yi Liu · Ke Sun · Bei Jiang · Linglong Kong

Dataset Distillation via Factorization

Songhua Liu · Kai Wang · Xingyi Yang · Jingwen Ye · Xinchao Wang

Oscillatory Tracking of Continuous Attractor Neural Networks Account for Phase Precession and Procession of Hippocampal Place Cells

Tianhao Chu · Zilong Ji · Junfeng Zuo · Wenhao Zhang · Tiejun Huang · Yuanyuan Mi · Si Wu

Palm up: Playing in the Latent Manifold for Unsupervised Pretraining

Hao Liu · Tom Zahavy · Volodymyr Mnih · Satinder Singh

Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning

Arsene Fansi Tchango · Rishab Goel · Julien Martel · Zhi Wen · Gaetan Marceau Caron · Joumana Ghosn

Large-Scale Retrieval for Reinforcement Learning

Peter Humphreys · Arthur Guez · Olivier Tieleman · Laurent Sifre · Theophane Weber · Timothy Lillicrap

What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

Shivam Garg · Dimitris Tsipras · Percy Liang · Gregory Valiant

Chain of Thought Imitation with Procedure Cloning

Mengjiao (Sherry) Yang · Dale Schuurmans · Pieter Abbeel · Ofir Nachum

First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces

Michael Jordan · Tianyi Lin · Emmanouil-Vasileios Vlatakis-Gkaragkounis

On the Identifiability of Nonlinear ICA: Sparsity and Beyond

Yujia Zheng · Ignavier Ng · Kun Zhang

Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators

Yuhan Helena Liu · Stephen Smith · Stefan Mihalas · Eric Shea-Brown · Uygar Sümbül

Nonlinear MCMC for Bayesian Machine Learning

James Vuckovic

Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture

Libin Zhu · Chaoyue Liu · Misha Belkin

Fast Algorithms for Packing Proportional Fairness and its Dual

Francisco Criado · David Martinez-Rubio · Sebastian Pokutta

Approaching Quartic Convergence Rates for Quasi-Stochastic Approximation with Application to Gradient-Free Optimization

Caio Kalil Lauand · Sean Meyn

Robust Generalized Method of Moments: A Finite Sample Viewpoint

Dhruv Rohatgi · Vasilis Syrgkanis

Adapting to Online Label Shift with Provable Guarantees

Yong Bai · Yu-Jie Zhang · Peng Zhao · Masashi Sugiyama · Zhi-Hua Zhou

Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries

Fabrizio Frasca · Beatrice Bevilacqua · Michael Bronstein · Haggai Maron

Reconstruction on Trees and Low-Degree Polynomials

Frederic Koehler · Elchanan Mossel

Benign Underfitting of Stochastic Gradient Descent

Tomer Koren · Roi Livni · Yishay Mansour · Uri Sherman

Using natural language and program abstractions to instill human inductive biases in machines

Sreejan Kumar · Carlos G. Correa · Ishita Dasgupta · Raja Marjieh · Michael Y Hu · Robert Hawkins · Jonathan D Cohen · nathaniel daw · Karthik Narasimhan · Tom Griffiths

Revisiting Injective Attacks on Recommender Systems

Haoyang LI · Shimin DI · Lei Chen

PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies

Guocheng Qian · Yuchen Li · Houwen Peng · Jinjie Mai · Hasan Hammoud · Mohamed Elhoseiny · Bernard Ghanem

SIXO: Smoothing Inference with Twisted Objectives

Dieterich Lawson · Allan Raventós · andrew warrington · Scott Linderman

Robust Graph Structure Learning via Multiple Statistical Tests

Yaohua Wang · Fangyi Zhang · Ming Lin · Senzhang Wang · Xiuyu Sun · Rong Jin

Fairness Reprogramming

Guanhua Zhang · Yihua Zhang · Yang Zhang · Wenqi Fan · Qing Li · Sijia Liu · Shiyu Chang

Private Graph All-Pairwise-Shortest-Path Distance Release with Improved Error Rate

Chenglin Fan · Ping Li · Xiaoyun Li

Optimal Comparator Adaptive Online Learning with Switching Cost

Zhiyu Zhang · Ashok Cutkosky · Yannis Paschalidis

Supervised Training of Conditional Monge Maps

Charlotte Bunne · Andreas Krause · Marco Cuturi

Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference

Richard Grumitt · Biwei Dai · Uros Seljak

CGLB: Benchmark Tasks for Continual Graph Learning

Xikun Zhang · Dongjin Song · Dacheng Tao

ADBench: Anomaly Detection Benchmark

Songqiao Han · Xiyang Hu · Hailiang Huang · Minqi Jiang · Yue Zhao

Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification

Ihsan Ullah · Dustin Carrión-Ojeda · Sergio Escalera · Isabelle Guyon · Mike Huisman · Felix Mohr · Jan N. van Rijn · Haozhe Sun · Joaquin Vanschoren · Phan Anh Vu

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

Kay Liu · Yingtong Dou · Yue Zhao · Xueying Ding · Xiyang Hu · Ruitong Zhang · Kaize Ding · Canyu Chen · Hao Peng · Kai Shu · Lichao Sun · Jundong Li · George H Chen · Zhihao Jia · Philip S Yu

Is one annotation enough? - A data-centric image classification benchmark for noisy and ambiguous label estimation

Lars Schmarje · Vasco Grossmann · Claudius Zelenka · Sabine Dippel · Rainer Kiko · Mariusz Oszust · Matti Pastell · Jenny Stracke · Anna Valros · Nina Volkmann · Reinhard Koch

Dungeons and Data: A Large-Scale NetHack Dataset

Eric Hambro · Roberta Raileanu · Danielle Rothermel · Vegard Mella · Tim Rocktäschel · Heinrich Küttler · Naila Murray

Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning

Hua Wei · Jingxiao Chen · Xiyang Ji · Hongyang Qin · Minwen Deng · Siqin Li · Liang Wang · Weinan Zhang · Yong Yu · Liu Linc · Lanxiao Huang · Deheng Ye · Qiang Fu · Wei Yang

EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations

Ahmad Darkhalil · Dandan Shan · Bin Zhu · Jian Ma · Amlan Kar · Richard Higgins · Sanja Fidler · David Fouhey · Dima Damen

EgoTaskQA: Understanding Human Tasks in Egocentric Videos

Baoxiong Jia · Ting Lei · Song-Chun Zhu · Siyuan Huang

SCAMPS: Synthetics for Camera Measurement of Physiological Signals

Daniel McDuff · Miah Wander · Xin Liu · Brian Hill · Javier Hernandez · Jonathan Lester · Tadas Baltrusaitis

Robustness Disparities in Face Detection

Samuel Dooley · George Z Wei · Tom Goldstein · John Dickerson

Long Range Graph Benchmark

Vijay Prakash Dwivedi · Ladislav Rampášek · Michael Galkin · Ali Parviz · Guy Wolf · Anh Tuan Luu · Dominique Beaini

CLEVRER-Humans: Describing Physical and Causal Events the Human Way

Jiayuan Mao · Xuelin Yang · Xikun Zhang · Noah Goodman · Jiajun Wu

CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks

Tejas Srinivasan · Ting-Yun Chang · Leticia Pinto Alva · Georgios Chochlakis · Mohammad Rostami · Jesse Thomason

OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics

Mohit Prabhushankar · Kiran Kokilepersaud · Yash-yee Logan · Stephanie Trejo Corona · Ghassan AlRegib · Charles Wykoff

SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained debugging and analysis

Roxana Daneshjou · Mert Yuksekgonul · Zhuo Ran Cai · Roberto Novoa · James Zou

Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment

Daniel Vera Nieto · Luigi Celona · Clara Fernandez Labrador

ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts

Saleh Ashkboos · Langwen Huang · Nikoli Dryden · Tal Ben-Nun · Peter Dueben · Lukas Gianinazzi · Luca Kummer · Torsten Hoefler

Hard ImageNet: Segmentations for Objects with Strong Spurious Cues

Mazda Moayeri · Sahil Singla · Soheil Feizi

Video compression dataset and benchmark of learning-based video-quality metrics

Anastasia Antsiferova · Sergey Lavrushkin · Maksim Smirnov · Aleksandr Gushchin · Dmitriy Vatolin · Dmitriy Kulikov

TweetNERD - End to End Entity Linking Benchmark for Tweets

Shubhanshu Mishra · Aman Saini · Raheleh Makki · Sneha Mehta · Aria Haghighi · Ali Mollahosseini

BigBio: A Framework for Data-Centric Biomedical Natural Language Processing

Jason Fries · Leon Weber · Natasha Seelam · Gabriel Altay · Debajyoti Datta · Samuele Garda · Sunny Kang · Rosaline Su · Wojciech Kusa · Samuel Cahyawijaya · Fabio Barth · Simon Ott · Matthias Samwald · Stephen Bach · Stella Biderman · Mario Sänger · Bo Wang · Alison Callahan · Daniel León Periñán · Théo Gigant · Patrick Haller · Jenny Chim · Jose Posada · John Giorgi · Karthik Rangasai Sivaraman · Marc Pàmies · Marianna Nezhurina · Robert Martin · Michael Cullan · Moritz Freidank · Nathan Dahlberg · Shubhanshu Mishra · Shamik Bose · Nicholas Broad · Yanis Labrak · Shlok Deshmukh · Sid Kiblawi · Ayush Singh · Minh Chien Vu · Trishala Neeraj · Jonas Golde · Albert Villanova del Moral · Benjamin Beilharz

Pythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case

Clément Chadebec · Louis Vincent · Stephanie Allassonniere

A Dataset for Efforts Towards Achieving the Sustainable Development Goal of Safe Working Environments

Eirik Lund Flogard · Ole Jakob Mengshoel

(f,Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics

Jeremiah Birrell · Paul Dupuis · Markos A. Katsoulakis · Yannis Pantazis · Luc Rey-Bellet

A Bregman Learning Framework for Sparse Neural Networks

Leon Bungert · Tim Roith · Daniel Tenbrinck · Martin Burger

D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data

Hai Shu · Zhe Qu · Hongtu Zhu

A Primer for Neural Arithmetic Logic Modules

Bhumika Mistry · Katayoun Farrahi · Jonathon Hare

[Re] Explaining in Style: Training a GAN to explain a classifier in StyleSpace

Chase van de Geijn · Victor Kyriacou · Irene Papadopoulou · Vasiliki Vasileiou

[Re] A Cluster-based Approach for Improving Isotropy in Contextual Embedding Space

Benjamin Džubur

[Re] Replication study of 'Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling'

Vera Neplenbroek · Sabijn Perdijk · Victor Prins

Go to Event Page

Social

Haben, The Deafblind Woman Who Conquered Harvard Law

Anoush Najarian

6:00 PM - 8:00 PM

We’d love to come together for an un-bookclub at NeurIPS 2022. We’ve been learning a lot in the cross-continental book club out of the book Haben: The Deafblind Woman Who Conquered Harvard Law.

We’d love to give you the gift of connection, conversation, and reflection the author and disability rights lawyer Haben Girma gave us. We ask participants to watch Haben's powerful talk at the US National Book Festival in preparation.

Join us for a discussion on accessibility and intersectionality, and the roles and responsibilities of the machine learning research community in building a world where disabled people thrive.

Sign-up form

... more

Social

K-Pop in NeurIPS

Jimin Lee

6:00 PM - 8:00 PM

Korean wave (aka K-wave or Hanryu) has become popular and familiar with global people. In particular, K-pop such as Butter and Gangnam-style and artists such as BTS and Blackpink are greatly loved by many global people around the world. We'd like to welcome researchers who love K-pop in NeurIPS to our social "K-pop in NeurIPS". We'd like to gather together and share our favorite K-pop, artists, and our special experiences related to K-pop. We expect that our Social would be an opportunity for researchers in NeurIPS around the world to become more intimate.

... more

Social

Negotiation Workshop: Feel More Confident Negotiating Your Next Offer in AI

Nicole Bannon

6:00 PM - 8:00 PM

"Join the team at Rora and 81cents, to get the tools, information, and data you need to negotiate your next offer in AI more confidently.

Some of the topics we'll cover in a 1.5 hr. period (with 1/2 an hour for Q&A) are:

- Understanding the fundamentals of compensation in tech (particularly around equity, bonus structures, etc.)
- How to get over your fears of negotiating
- How to decide which company / offer is right for you
- How to negotiate without counter offers and without knowing ""market value""
- How to respond to pushback from recruiters and other guilt tripping / lowballing /pressure tactics
- How to avoid having an offer rescinded
- How to negotiate deadline of an offer
- Walking through a timeline of the negotiation process for a new offer"

... more

Social

Open Mic Night

John Dickerson

6:00 PM - 8:00 PM

"Machine learning is a field without ongoing feuds, without heterogeneity of thought, without competing opinions, and without uncertainty over where the field is and where it is going. We all know this. We all use the same tools, have the same opinion about which libraries are the best, and what tools are best for tracking at train time. Yet, in this social event, we aim to find that rare soul in the AI/ML community with a primarily technical opinion that goes against the status quo, to give them a stage, and to curate engagement with an audience of NeurIPS members. Is symbolic reasoning dead? Who knows – but this discussion sure won’t be. Our goal here is to encourage a lively discussion, while ensuring that the speakers adhere to the NeurIPS Code of Conduct; in particular, by keeping the discussion respectful and professional.

The first half-hour of this social will be a reception, during which the organizers will pass around a sign-up for topics and for participants. We will ensure that the mic is accessible to everyone who wishes to participate, and we will aim for a diverse range of ideas, perspectives, and demographics. Then, we will have several rounds of open mic debates and response. The winners of each debate -- chosen by the audience -- will have eternal bragging rights."

... more

Social

Interdisciplinary ML Mixer

Patrick Perrine · Jose Mierzejewski

6:00 PM - 8:00 PM

Given the many disciplines that encompass ML/DL, it is important that we as researchers better understand academics with differing backgrounds than our own to produce valued contributions. In this in-person 2-hour social, we pair participants together based on differing levels of experience in related disciplines of ML/DL. These pairings would be determined by having participants write their domain of experience on a nametag, then asking participants to find and chat with another person with differing experience. For example, suppose Researcher A identifies as being highly experienced in Neuroscience but has little to no experience in Semi-Supervised Learning. Researcher A could then be paired with Researcher B, who has a great background in Semi-Supervised Learning but has had no exposure to Neuroscience. These interactions could allow us to form better connections across disciplines and build a better understanding of the vast landscape of ML/DL.

If you attended our social, please fill out this feedback form: https://forms.gle/TE8oKwdYCDhxLjpk6

... more