This week marks the beginning of the 35th annual Conference on Neural Information Processing Systems (NeurIPS 2021), the biggest machine learning conference of the year. NeurIPS 2021 will be held virtually and includes invited talks, demonstrations and presentations of some of the latest in machine learning research. This year, NeurIPS also announced a new Datasets and Benchmarks track, which will include publications, talks, posters, and discussions related to this research area.

Google will have a strong presence with more than 170 accepted papers, additionally contributing to and learning from the broader academic research community via talks, posters, workshops, and tutorials. You can learn more about our work being presented in the list below (Google affiliations highlighted in bold).

Organizing Committee

Communications Co-Chair: Emily Denton
Program Co-Chair: Yann Dauphin
Workshop Co-Chair: Sanmi Koyejo

Senior Area Chairs: Alekh Agarwal, Amir Globerson, Been Kim, Charles Sutton, Claudio Gentile, Corinna Cortes, Dale Schuurmans, David Duvenaud, Elad Hazan, Hugo Larochelle, Jean-Philippe Vert, Kevin Murphy, Marco Cuturi, Mehryar Mohri, Mohammad Ghavamzadeh, Samory Kpotufe, Sanjiv Kumar, Satyen Kale, Sergey Levine, Tara N. Sainath, Yishay Mansour

Area Chairs: Abhishek Kumar, Abhradeep Guha Thakurta, Alex Kulesza, Alexander A. Alemi, Alexander T. Toshev, Amin Karbasi, Amit Daniely, Ananda Theertha Suresh, Ankit Singh Rawat, Ashok Cutkosky, Badih Ghazi, Balaji Lakshminarayanan, Ben Poole, Bo Dai, Boqing Gong, Chelsea Finn, Chiyuan Zhang, Christian Szegedy, Cordelia Schmid, Craig Boutilier, Cyrus Rashtchian, D. Sculley, Daniel Keysers, David Ha, Denny Zhou, Dilip Krishnan, Dumitru Erhan, Dustin Tran, Ekin Dogus Cubuk, Fabian Pedregosa, George Tucker, Hanie Sedghi, Hanjun Dai, Heinrich Jiang, Hossein Mobahi, Izhak Shafran, Jaehoon Lee, Jascha Sohl-Dickstein, Jasper Snoek, Jeffrey Pennington, Jelani Nelson, Jieming Mao, Justin Gilmer, Karol Hausman, Karthik Sridharan, Kevin Swersky, Maithra Raghu, Mario Lucic, Mathieu Blondel, Matt Kusner, Matthew Johnson, Matthieu Geist, Ming-Hsuan Yang, Mohammad Mahdian, Mohammad Norouzi, Nal Kalchbrenner, Naman Agarwal, Nicholas Carlini, Nicolas Papernot, Olivier Bachem, Olivier Pietquin, Paul Duetting, Praneeth Netrapalli, Pranjal Awasthi, Prateek Jain, Quentin Berthet, Renato Paes Leme, Richard Nock, Rif A. Saurous, Rose Yu, Roy Frostig, Samuel Stern Schoenholz, Sashank J. Reddi, Sercan O. Arik, Sergei Vassilvitskii, Sergey Ioffe, Shay Moran, Silvio Lattanzi, Simon Kornblith, Srinadh Bhojanapalli, Thang Luong, Thomas Steinke, Tim Salimans, Tomas Pfister, Tomer Koren, Uri Stemmer, Vahab Mirrokni, Vikas Sindhwani, Vincent Dumoulin, Virginia Smith, Vladimir Braverman, W. Ronny Huang, Wen Sun, Yang Li, Yasin Abbasi-Yadkori, Yinlam Chow,Yujia Li, Yunhe Wang, Zoltán Szabó

NeurIPS Foundation Board 2021: Michael Mozer, Corinna Cortes, Hugo Larochelle, John C. Platt, Fernando Pereira

Test of Time Award

Online Learning for Latent Dirichlet Allocation
Matthew D. Hoffman, David M. Blei, Francis Bach

Publications

Deep Reinforcement Learning at the Edge of the Statistical Precipice (see blog post)
Outstanding Paper Award Recipient
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, Marc G. Bellemare

A Separation Result Between Data-Oblivious and Data-Aware Poisoning Attacks
Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Guha Thakurta

Adversarial Robustness of Streaming Algorithms Through Importance Sampling
Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou

Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery
Mugallodi Rakesh, Jogendra Nath Kundu, Varun Jampani, R. Venkatesh Babu

Attention Bottlenecks for Multimodal Fusion
Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid, Chen Sun

Autonomous Reinforcement Learning via Subgoal Curricula
Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn

Calibration and Consistency of Adversarial Surrogate Losses
Pranjal Awasthi, Natalie S. Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong

Compressive Visual Representations
Kuang-Huei Lee, Anurag Arnab, Sergio Guadarrama, John Canny, Ian Fischer

Counterfactual Invariance to Spurious Correlations in Text Classification
Victor Veitch, Alexander D’Amour, Steve Yadlowsky, Jacob Eisenstein

Deep Learning Through the Lens of Example Difficulty
Robert J.N. Baldock, Hartmut Maennel, Behnam Neyshabur

Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Vinent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande

Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning
Ligeng Zhu, Hongzhou Lin, Yao Lu, Yujun Lin, Song Han

Discrete-Valued Neural Communication
Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, Yoshua Bengio

Do Vision Transformers See Like Convolutional Neural Networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy

Dueling Bandits with Team Comparisons
Lee Cohen, Ulrike Schmidt-Kraepelin, Yishay Mansour

End-to-End Multi-Modal Video Temporal Grounding
Yi-Wen Chen, Yi-Hsuan Tsai, Ming-Hsuan Yang

Environment Generation for Zero-Shot Compositional Reinforcement Learning
Izzeddin Gur, Natasha Jaques, Yingjie Miao, Jongwook Choi, Manoj Tiwari, Honglak Lee, Aleksandra Faust

H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motion
Hongyi Xu, Thiemo Alldieck, Cristian Sminchisescu

Improving Calibration Through the Relationship with Adversarial Robustness
Yao Qin, Xuezhl Wang, Alex Beutel, Ed Chi

Learning Generalized Gumbel-Max Causal Mechanisms
Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan

MICo: Improved Representations via Sampling-Based State Similarity for Markov Decision Processes
Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland

Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness
Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif

Neural Circuit Synthesis from Specification Patterns
Frederik Schmitt, Christopher Hahn, Markus N. Rabe, Bernd Finkbeiner

Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi, Pradyumna YM, Varun Jampani, Anirban Chakraborty, R. Venkatesh Babu

Object-Aware Contrastive Learning for Debiased Scene Representation
Sangwoo Mo, Hyunwoo Kang, Kihyuk Soh, Chun-Liang Li, Jinwoo Shin

On Density Estimation with Diffusion Models
Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho

On Margin-Based Cluster Recovery with Oracle Queries
Marco Bressan, Nicolo Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice

On Model Calibration for Long-Tailed Object Detection and Instance Segmentation
Tai-Yu Pan, Cheng Zhang, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao

Parallelizing Thompson Sampling
Amin Karbasi, Vahab Mirrokni, Mohammad Shadravan

Reverse-Complement Equivariant Networks for DNA Sequences
Vincent Mallet, Jean-Philippe Vert

Revisiting ResNets: Improved Training and Scaling Strategies
Irwan Bello, William Fedus, Xianzhi Du, Ekin Dogus Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph

Revisiting the Calibration of Modern Neural Networks
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Ann Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic

Scaling Vision with Sparse Mixture of Experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, Neil Houlsby

SE(3)-Equivariant Prediction of Molecular Wavefunctions and Electronic Densities
Oliver Thorsten Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt, Klaus Robert Muller

Stateful ODE-Nets Using Basis Function Expansions
Alejandro Francisco Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney

Statistically and Computationally Efficient Linear Meta-Representation Learning
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

Streaming Belief Propagation for Community Detection
Yuchen Wu, Jakab Tardos, Mohammad Hossein Bateni, André Linhares, Filipe Miguel Gonçalves de Almeida, Andrea Montanari, Ashkan Norouzi-Fard

Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls
Nick Doudchenko, Khashayar Khosravi, Jean Pouget-Abadie, Sebastien Lahaie, Miles Lubin, Vahab Mirrokni, Jann Spiess, Guido Imbens

The Difficulty of Passive Learning in Deep Reinforcement Learning
George Ostrovski, Pablo Samuel Castro, Will Dabney

The Pareto Frontier of Model Selection for General Contextual Bandits
Teodor Marinov, Julian Zimmert

VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, Boqing Gong

Co-Adaptation of Algorithmic and Implementational Innovations in Inference-Based Deep Reinforcement Learning
Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, Shixiang Gu

Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn

Does Knowledge Distillation Really Work?
Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson

Exponential Graph is Provably Efficient for Decentralized Deep Training
Bicheng Ying, Kun Yuan, Yiming Chen, Hanbin Hu, Pan Pan, Wotao Yin

Faster Matchings via Learned Duals
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii

Improved Transformer for High-Resolution GANs
Long Zhao, Zizhao Zhang, Ting Chen, Dimitris N. Metaxas, Han Zhang

Near-Optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems
Prateek Jain, Suhas S. Kowshik, Dheeraj Mysore Nagaraj, Praneeth Netrapalli

Nearly Horizon-Free Offline Reinforcement Learning
Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi

Overparameterization Improves Robustness to Covariate Shift in High Dimensions
Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington

Pay Attention to MLPs
Hanxiao Liu, Zihang Dai, David R. So, Quoc V. Le

PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair
Zimin Chen*, Vincent Josua Hellendoorn*, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra

Prior-Independent Dynamic Auctions for a Value-Maximizing Buyer
Yuan Deng, Hanrui Zhang

Remember What You Want to Forget: Algorithms for Machine Unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh

Reverse Engineering Learned Optimizers Reveals Known and Novel Mechanisms
Niru Maheswaranathan*, David Sussillo*, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein

Revisiting 3D Object Detection From an Egocentric Perspective
Boyang Deng, Charles R. Qi, Mahyar Najibi, Thomas Funkhouser, Yin Zhou, Dragomir Anguelov

Robust Auction Design in the Auto-Bidding World
Santiago Balseiro, Yuan Deng, Jieming Mao, Vahab Mirrokni, Song Zuo

Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data
Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi

Understanding How Encoder-Decoder Architectures Attend
Kyle Aitken, Vinay V. Ramasesh, Yuan Cao, Niru Maheswaranathan

Understanding the Effect of Stochasticity in Policy Optimization
Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvari, Dale Schuurmans

Accurately Solving Rod Dynamics with Graph Learning
Han Shao, Tassilo Kugelstadt, Torsten Hädrich, Wojtek Palubicki, Jan Bender, Sören Pirk, Dominik L. Michels

GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein

Learnability of Linear Thresholds from Label Proportions
Rishi Saket

MLP-Mixer: An All-MLP Architecture for Vision
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy

Neural Additive Models: Interpretable Machine Learning with Neural Nets
Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, Geoffrey Hinton

Neural Production Systems
Anirudh Goyal, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio

Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling
Niv Giladi, Zvika Ben-Haim, Sella Nevo, Yossi Matias, Daniel Soudry

Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects
Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Marc Pollefeys

What Matters for Adversarial Imitation Learning?
Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz

A Convergence Analysis of Gradient Descent on Graph Neural Networks
Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi

A Geometric Analysis of Neural Collapse with Unconstrained Features
Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu

Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan

Controlled Text Generation as Continuous Optimization with Multiple Constraints
Sachin Kumar, Eric Malmi, Aliaksei Severyn, Yulia Tsvetkov

Coupled Gradient Estimators for Discrete Latent Variables
Zhe Dong, Andriy Mnih, George Tucker

Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-Training Ensembles
Jiefeng Chen*, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, Somesh Jha

Neural Active Learning with Performance Guarantees
Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile

Optimal Sketching for Trace Estimation
Shuli Jiang, Hai Pham, David Woodruff, Qiuyi (Richard) Zhang

Representing Long-Range Context for Graph Neural Networks with Global Attention
Zhanghao Wu, Paras Jain, Matthew A. Wright, Azalia Mirhoseini, Joseph E. Gonzalez, Ion Stoica

Scaling Up Exact Neural Network Compression by ReLU Stability
Thiago Serra, Xin Yu, Abhinav Kumar, Srikumar Ramalingam

Soft Calibration Objectives for Neural Networks
Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael Curtis Mozer, Rebecca Roelofs

Sub-Linear Memory: How to Make Performers SLiM
Valerii Likhosherstov, Krzysztof Choromanski, Jared Davis, Xingyou Song, Adrian Weller

A New Theoretical Framework for Fast and Accurate Online Decision-Making
Nicolò Cesa-Bianchi, Tommaso Cesari, Yishay Mansour, Vianney Perchet

Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning
Nan Ding, Xi Chen, Tomer Levinboim, Sebastian Goodman, Radu Soricut

Differentially Private Multi-Armed Bandits in the Shuffle Model
Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer

Efficient and Local Parallel Random Walks
Michael Kapralov, Silvio Lattanzi, Navid Nouri, Jakab Tardos

Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss
Michael Louis Iuzzolino, Michael Curtis Mozer, Samy Bengio*

It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems
Regev Cohen, Yochai Blau, Daniel Freedman, Ehud Rivlin

Learning to Combine Per-Example Solutions for Neural Program Synthesis
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow

LLC: Accurate, Multi-purpose Learnt Low-Dimensional Binary Codes
Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham Kakade, Ali Farhadi

There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning (see blog post)

Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist

A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models
Ibrahim Alabdulmohsin, Mario Lucic

Adaptive Sampling for Minimax Fair Classification
Shubhanshu Shekhar, Greg Fields, Mohammad Ghavamzadeh, Tara Javidi

Asynchronous Stochastic Optimization Robust to Arbitrary Delays
Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain

Boosting with Multiple Sources
Corinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha Suresh

Breaking the Centralized Barrier for Cross-Device Federated Learning
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stitch, Ananda Theertha Sureshi

Canonical Capsules: Self-Supervised Capsules in Canonical Pose
Weiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey Hinton, Kwang Moo Yi

Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasi Manurangsi, Renato Paes Leme, Jon Schneider

Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee|Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch

Deep Learning on a Data Diet: Finding Important Examples Early in Training
Mansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite

Deep Learning with Label Differential Privacy
Badih Ghazi, Noah Golowich*, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

Efficient Training of Retrieval Models Using Negative Cache
Erik Lindgren, Sashank J. Reddi, Ruiqi Guo, Sanjiv Kumar

Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing
Yan-Bo Lin, Hung-Yu Tseng, Hsin-Ying Lee, Yen-Yu Lin, Ming-Hsuan Yang

Federated Reconstruction: Partially Local Federated Learning
Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, Sushant Prakash

Framing RNN as a Kernel Method: A Neural ODE Approach
Adeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard Biau

Learning Semantic Representations to Verify Hardware Designs
Shobha Vasudevan, Wenjie Jiang, David Bieber, Rishabh Singh, Hamid Shojaei, C. Richard Ho, Charles Sutton

Learning with User-Level Privacy
Daniel Asher Nathan Levy*, Ziteng Sun*, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh

Logarithmic Regret from Sublinear Hints
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

Margin-Independent Online Multiclass Learning via Convex Geometry
Guru Guruganesh, Allen Liu, Jon Schneider, Joshua Ruizhi Wang

Multiclass Boosting and the Cost of Weak Learning
Nataly Brukhim, Elad Hazan, Shay Moran, Indraneel Mukherjee, Robert E. Schapire

Neural-PIL: Neural Pre-integrated Lighting for Reflectance Decomposition
Mark Boss, Varun Jampani, Raphael Braun, Ce Liu*, Jonathan T. Barron, Hendrik Lensch

Never Go Full Batch (in Stochastic Convex Optimization)
Idan Amir, Yair Carmon, Tomer Koren, Roi Livni

On Large-Cohort Training for Federated Learning
Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith

On the Sample Complexity of Privately Learning Axis-Aligned Rectangles
Menachem Sadigurschi, Uri Stemmer

Online Control of Unknown Time-Varying Dynamical Systems
Edgar Minasyan, Paula Gradu, Max Simchowitz, Elad Hazan

Online Knapsack with Frequency Predictions
Sungjin Im, Ravi Kumar,Mahshid Montazer Qaem, Manish Purohit

Optimal Rates for Random Order Online Optimization
Uri Sherman, Tomer Koren, Yishay Mansour

Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure
Aviv Rosenberg, Yishay Mansour

Practical Large-Scale Linear Programming Using Primal-Dual Hybrid Gradient
David Applegate, Mateo Díaz*, Oliver Hinder, Haihao Lu*, Miles Lubin, Brendan O’Donoghue, Warren Schudy

Private and Non-Private Uniformity Testing for Ranking Data
Robert Istvan Busa-Fekete, Dimitris Fotakis, Manolis Zampetakis

Privately Learning Subspaces
Vikrant Singhal, Thomas Steinke

Provable Representation Learning for Imitation with Contrastive Fourier Features
Ofir Nachum, Mengjiao Yang

Safe Reinforcement Learning with Natural Language Constraints
Tsung-Yen Yang, Michael Hu, Yinlam Chow, Peter J. Ramadge, Karthik Narasimhan

Searching for Efficient Transformers for Language Modeling
David R. So, Wojciech Mańke, Hanxiao Liu, Zihang Dai, Noam Shazeer, Quoc V. Le

SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression
Steve Yadlowsky, Taedong Yun, Cory McLean, Alexander D’Amour

Streaming Linear System Identification with Reverse Experience Replay
Prateek Jain, Suhas S. Kowshik, Dheeraj Mysore Nagaraj, Praneeth Netrapalli

The Skellam Mechanism for Differentially Private Federated Learning
Naman Agarwal, Peter Kairouz, Ziyu Liu*

TokenLearner: Adaptive Space-Time Tokenization for Videos
Michael S. Ryoo, AJ Piergiovanni, Anurag Arnab, Mostafa Dehghani, Anelia Angelova

Towards Best-of-All-Worlds Online Learning with Feedback Graphs
Liad Erez, Tomer Koren

Training Over-Parameterized Models with Non-decomposable Objectives
Harikrishna Narasimhan, Aditya Krishna Menon

Twice Regularized MDPs and the Equivalence Between Robustness and Regularization
Esther Derman, Matthieu Geist, Shie Mannor

Unsupervised Learning of Compositional Energy Concepts
Yilun Du, Shuang Li, Yash Sharma, Joshua B. Tenenbaum, Igor Mordatch

User-Level Differentially Private Learning via Correlated Sampling
Badih Ghazi, Ravi Kumar, Pasin Manurangsi

ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction
Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Ce Liu*, Deva Ramanan

A Minimalist Approach to Offline Reinforcement Learning
Scott Fujimoto, Shixiang Gu

A Unified View of cGANs With and Without Classifiers
Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin

CoAtNet: Marrying Convolution and Attention for All Data Sizes (see blog post)
Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan

Combiner: Full Attention Transformer with Sparse Computation Cost
Hongyu Ren*, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai

Contrastively Disentangled Sequential Variational Autoencoder
Junwen Bai, Weiran Wang, Carla P. Gomes

Controlling Neural Networks with Rule Representations
Sungyong Seo, Sercan O. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister

Dataset Distillation with Infinitely Wide Convolutional Networks
Timothy Nguyen*, Roman Novak, Lechao Xiao, Jaehoon Lee

Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess
Gregory Clark

Differentially Private Learning with Adaptive Clipping
Galen Andrew, Om Thakkar, Swaroop Ramaswamy, Hugh Brendan McMahan

Differentially Private Model Personalization
Prateek Jain, Keith Rush, Adam Smith, Shuang Song, Abhradeep Thakurta

Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations
Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan

Efficiently Identifying Task Groupings for Multi-Task Learning
Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn

Generalized Shape Metrics on Neural Representations
Alex H. Williams, Erin Kunz, Simon Kornblith, Scott Linderman

High-Probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails
Ashok Cutkosky, Harsh Mehta

Identity Testing for Mallows Model
Róbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi, Manolis Zampetakis

Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding
Yang Li, Si Si, Gang Li, Cho-Jui Hsieh, Samy Bengio*

Learning to Select Exogenous Events for Marked Temporal Point Process
Ping Zhang, Rishabh K. Iyer, Ashish V. Tendulkar, Gaurav Aggarwal, Abir De

Meta-learning to Improve Pre-training
Aniruddh Raghu, Jonathan Peter Lorraine, Simon Kornblith, Matthew B.A. McDermott, David Duvenaud

Pointwise Bounds for Distribution Estimation Under Communication Constraints
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür

REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People Under Weak Supervision
Mihai Fieraru, Mihai Zanfir, Teodor Alexandru Szente, Eduard Gabriel Bazavan, Vlad Olaru, Cristian Sminchisescu

Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification
Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov

Revealing and Protecting Labels in Distributed Training
Trung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays

Robust Predictable Control
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Robust Visual Reasoning via Language Guided Neural Module Networks
Arjun Reddy Akula, Varun Jampani, Soravit Changpinyo, Song-Chun Zhu

Towards Understanding Retrosynthesis by Energy-Based Models
Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai

Exploring the Limits of Out-of-Distribution Detection
Stanislav Fort, Jie Ren, Balaji Lakshminarayanan

Minimax Regret for Stochastic Shortest Path
Alon Cohen, Yonathan Efroni, Yishay Mansour, Aviv Rosenberg

No Regrets for Learning the Prior in Bandits
Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvari

Structured Denoising Diffusion Models in Discrete State-Spaces
Jacob Austin, Daniel D. Johnsonv, Jonathan Ho, Daniel Tarlow, Rianne van den Berg

The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning (see blog post)
Yujin Tang, David Ha

On the Existence of The Adversarial Bayes Classifier
Pranjal Awasthi, Natalie Frank, Mehyrar Mohri

Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
Christopher Dann, Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert

A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning
Christopher Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert

Datasets & Benchmarks Accepted Papers

Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research
Bernard Koch, Emily Denton, Alex Hanna, Jacob G. Foster
Datasets & Benchmarks Best Paper

Constructing a Visual Dataset to Study the Effects of Spatial Apartheid in South Africa
Raesetje Sefala, Timnit Gebru, Luzango Mfupe, Nyalleng Moorosi

AI and the Everything in the Whole Wide World Benchmark
Inioluwa Deborah Raji, Emily M. Bender, Amandalynne Paullada, Emily Denton, Alex Hannah

A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches
Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, Hugo Larochelle

The Neural MMO Platform for Massively Multi-agent Research
Joseph Suarez, Yilun Du, Clare Zhu, Igor Mordatch, Phillip Isola

Systematic Evaluation of Causal Discovery in Visual Model-Based Reinforcement Learning
Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajole, Stefan Bauer, Danilo Rezende, Yoshua Bengio, Michael Mozer, Christopher Pal

STEP: Segmenting and Tracking Every Pixel
Mark Weber, Jun Xie, Maxwell Collins, Yukun Zhu, Paul Voigtlaender, Hartwig Adam, Bradley Green, Andreas Geiger, Bastian Leibe, Daneil Cremers, Aljosa Osep, Laura Leal-Taixe, Liang-Chieh Chen

Artsheets for Art Datasets
Ramya Srinivisan, Emily Denton, Jordan Famularo, Negar Rostamzadeh, Fernando Diaz, Beth Coleman

SynthBio: A Case in Human–AI Collaborative Curation of Text Datasets
Ann Yuan, Daphne Ippolito, Vitaly Niolaev, Chris Callison-Burch, Andy Coenen, Sebastian Gehrmann

Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal

Brax – A Differentiable Physics Engine for Large Scale Rigid Body Simulation (see blog post)
C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem

MLPerf Tiny Benchmark
Colby Banbury, Vijay Janapa Reddi, Peter Torelli, Jeremy Holleman, Nat Jeffries, Csaba Kiraly, Pietro Montino, David Kanter, Sebastian Ahmed, Danilo Pau, Urmish Thakker, Antonio Torrini, Peter Warden, Jay Cordaro, Giuseppe Di Guglielmo, Javier Duarte, Stephen Gibellini, Videet Parekh, Honson Tran, Nhan Tran, Niu Wenxu, Xu Xuesong

Automatic Construction of Evaluation Suites for Natural Language Generation Datasets
Simon Mille, Kaustubh D. Dhole, Saad Mahamood, Laura Perez-Beltrachini, Varun Gangal, Mihir Kale, Emiel van Miltenburg, Sebastian Gehrmann

An Empirical Investigation of Representation Learning for Imitation
Xin Chen, Sam Toyer, Cody Wild, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven Wang, Ping Luo, Stuart Russell, Pieter Abbeel, Rohin Shah

Multilingual Spoken Words Corpus
Mark Mazumder, Sharad Chitlangia, Colby Banbury, Yiping Kang, Juan Manuel Ciro, Keith Achorn, Daniel Galvez, Mark Sabini, Peter Mattson, David Kanter, Greg Diamos, Pete Warden, Josh Meyer, Vijay Janapa Reddi

Workshops

4th Robot Learning Workshop: Self-Supervised and Lifelong Learning
Sponsor: Google
Organizers include Alex Bewley, Vincent Vanhoucke

Differentiable Programming Workshop
Sponsor: Google

Machine Learning for Creativity and Design
Sponsor: Google
Organizers include: Daphne Ippolito, David Ha

LatinX in AI (LXAI) Research @ NeurIPS 2021
Sponsor: Google
Sponsorship Level: Platinum
Workshop Chairs include: Andres Munoz Medina
Mentorship Roundtables include: Jonathan Huang, Pablo Samuel Castro

Algorithmic Fairness Through the Lens of Causality and Robustness
Organizers include: Jessica Schrouff, Awa Dieng

ImageNet: Past, Present, and Future
Organizers include: Lucas Beyer, Xiaohua Zhai
Speakers include: Emily Denton, Vittorio Ferrari, Alex Hanna, Alex Kolesnikov, Rebecca Roelofs

Optimal Transport and Machine Learning
Organizers include: Marco Cuturi

Safe and Robust Control of Uncertain Systems
Speakers include: Aleksandra Faust

CtrlGen: Controllable Generative Modeling in Language and Vision
Speakers include: Sebastian Gehrmann

Deep Reinforcement Learning
Organizers include: Chelsea Finn
Speakers include: Karol Hausam, Dale Schuurmans

Distribution Shifts: Connecting Methods and Applications (DistShift)
Speakers include: Chelsea Finn

ML For Systems
Organizers include: Anna Goldie, Martin Maas, Azade Nazi, Azalia Mihoseini, Milad Hashemi, Kevin Swersky

Learning in Presence of Strategic Behavior
Organizers include: Yishay Mansour

Bayesian Deep Learning
Organizers include: Zoubin Ghahramani, Kevin Murphy

Advances in Programming Languages and Neurosymbolic Systems (AIPLANS)
Organizers include: Disha Shrivastava, Vaibhav Tulsyan, Danny Tarlow

Ecological Theory of Reinforcement Learning: How Does Task Design Influence Agent Learning?
Organizers include: Shixiang Shane Gu, Pablo Samuel Castro, Marc G. Bellemare

The Symbiosis of Deep Learning and Differential Equations
Organizers include: Lily Hu

Out-of-Distribution Generalization and Adaptation in Natural and Artificial Intelligence
Speakers include: Chelsea Finn

Cooperative AI
Organizers include: Natasha Jaques

Offline Reinforcement Learning
Organizers include: Rishabh Agarwal, George Tucker
Speakers include: Minmin Chen

2nd Workshop on Self-Supervised Learning: Theory and Practice
Organizers include: Kristina Toutanova

Data Centric AI
Organizers include: Lora Aroyo

Math AI for Education (MATHAI4ED): Bridging the Gap Between Research and Smart Education
Organizers include: Yuhai (Tony) Wu

Tutorials

Beyond Fairness in Machine Learning
Organizers include: Emily Denton

Competitions

Evaluating Approximate Inference in Bayesian Deep Learning
Organizers include: Matthew D. Hoffman, Sharad Vikram

HEAR 2021 NeurIPS Challenge Holistic Evaluation of Audio Representations
Organizers include: Jesse Engel

Machine Learning for Combinatorial Optimization
Organizers include: Pawel Lichocki, Miles Lubin


*Work done while at Google.  ↩

Currently at Google.  ↩

Posted by Jaqui Herman and Cat Armato, Program Managers This week marks the beginning of the 35th annual Conference on Neural Information Processing Systems (NeurIPS 2021), the biggest machine learning conference of the year. NeurIPS 2021 will be held virtually and includes invited talks, demonstrations and presentations of some of the latest in machine learning research. This year, NeurIPS also announced a new Datasets and Benchmarks track, which will include publications, talks, posters, and discussions related to this research area. Google will have a strong presence with more than 170 accepted papers, additionally contributing to and learning from the broader academic research community via talks, posters, workshops, and tutorials. You can learn more about our work being presented in the list below (Google affiliations highlighted in bold). Organizing CommitteeCommunications Co-Chair: Emily DentonProgram Co-Chair: Yann DauphinWorkshop Co-Chair: Sanmi Koyejo Senior Area Chairs: Alekh Agarwal, Amir Globerson, Been Kim, Charles Sutton, Claudio Gentile, Corinna Cortes, Dale Schuurmans, David Duvenaud, Elad Hazan, Hugo Larochelle, Jean-Philippe Vert, Kevin Murphy, Marco Cuturi, Mehryar Mohri, Mohammad Ghavamzadeh, Samory Kpotufe, Sanjiv Kumar, Satyen Kale, Sergey Levine, Tara N. Sainath, Yishay Mansour Area Chairs: Abhishek Kumar, Abhradeep Guha Thakurta, Alex Kulesza, Alexander A. Alemi, Alexander T. Toshev, Amin Karbasi, Amit Daniely, Ananda Theertha Suresh, Ankit Singh Rawat, Ashok Cutkosky, Badih Ghazi, Balaji Lakshminarayanan, Ben Poole, Bo Dai, Boqing Gong, Chelsea Finn, Chiyuan Zhang, Christian Szegedy, Cordelia Schmid, Craig Boutilier, Cyrus Rashtchian, D. Sculley, Daniel Keysers, David Ha, Denny Zhou, Dilip Krishnan, Dumitru Erhan, Dustin Tran, Ekin Dogus Cubuk, Fabian Pedregosa, George Tucker, Hanie Sedghi, Hanjun Dai, Heinrich Jiang, Hossein Mobahi, Izhak Shafran, Jaehoon Lee, Jascha Sohl-Dickstein, Jasper Snoek, Jeffrey Pennington, Jelani Nelson, Jieming Mao, Justin Gilmer, Karol Hausman, Karthik Sridharan, Kevin Swersky, Maithra Raghu, Mario Lucic, Mathieu Blondel, Matt Kusner, Matthew Johnson, Matthieu Geist, Ming-Hsuan Yang, Mohammad Mahdian, Mohammad Norouzi, Nal Kalchbrenner, Naman Agarwal, Nicholas Carlini, Nicolas Papernot, Olivier Bachem, Olivier Pietquin, Paul Duetting, Praneeth Netrapalli, Pranjal Awasthi, Prateek Jain, Quentin Berthet, Renato Paes Leme, Richard Nock, Rif A. Saurous, Rose Yu, Roy Frostig, Samuel Stern Schoenholz, Sashank J. Reddi, Sercan O. Arik, Sergei Vassilvitskii, Sergey Ioffe, Shay Moran, Silvio Lattanzi, Simon Kornblith, Srinadh Bhojanapalli, Thang Luong, Thomas Steinke, Tim Salimans, Tomas Pfister, Tomer Koren, Uri Stemmer, Vahab Mirrokni, Vikas Sindhwani, Vincent Dumoulin, Virginia Smith, Vladimir Braverman, W. Ronny Huang, Wen Sun, Yang Li, Yasin Abbasi-Yadkori, Yinlam Chow,Yujia Li, Yunhe Wang, Zoltán Szabó NeurIPS Foundation Board 2021: Michael Mozer, Corinna Cortes, Hugo Larochelle, John C. Platt, Fernando Pereira Test of Time AwardOnline Learning for Latent Dirichlet Allocation Matthew D. Hoffman†, David M. Blei, Francis Bach PublicationsDeep Reinforcement Learning at the Edge of the Statistical Precipice (see blog post)Outstanding Paper Award Recipient Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, Marc G. Bellemare A Separation Result Between Data-Oblivious and Data-Aware Poisoning AttacksSamuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Guha Thakurta Adversarial Robustness of Streaming Algorithms Through Importance SamplingVladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou Aligning Silhouette Topology for Self-Adaptive 3D Human Pose RecoveryMugallodi Rakesh, Jogendra Nath Kundu, Varun Jampani, R. Venkatesh Babu Attention Bottlenecks for Multimodal FusionArsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid, Chen Sun Autonomous Reinforcement Learning via Subgoal CurriculaArchit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn Calibration and Consistency of Adversarial Surrogate LossesPranjal Awasthi, Natalie S. Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong Compressive Visual RepresentationsKuang-Huei Lee, Anurag Arnab, Sergio Guadarrama, John Canny, Ian Fischer Counterfactual Invariance to Spurious Correlations in Text ClassificationVictor Veitch, Alexander D’Amour, Steve Yadlowsky, Jacob Eisenstein Deep Learning Through the Lens of Example DifficultyRobert J.N. Baldock, Hartmut Maennel, Behnam Neyshabur Deep Neural Networks as Point Estimates for Deep Gaussian ProcessesVinent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande Delayed Gradient Averaging: Tolerate the Communication Latency for Federated LearningLigeng Zhu, Hongzhou Lin, Yao Lu, Yujun Lin, Song Han Discrete-Valued Neural CommunicationDianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, Yoshua Bengio Do Vision Transformers See Like Convolutional Neural Networks?Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy Dueling Bandits with Team ComparisonsLee Cohen, Ulrike Schmidt-Kraepelin, Yishay Mansour End-to-End Multi-Modal Video Temporal GroundingYi-Wen Chen, Yi-Hsuan Tsai, Ming-Hsuan Yang Environment Generation for Zero-Shot Compositional Reinforcement LearningIzzeddin Gur, Natasha Jaques, Yingjie Miao, Jongwook Choi, Manoj Tiwari, Honglak Lee, Aleksandra Faust H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in MotionHongyi Xu, Thiemo Alldieck, Cristian Sminchisescu Improving Calibration Through the Relationship with Adversarial RobustnessYao Qin, Xuezhl Wang, Alex Beutel, Ed Chi Learning Generalized Gumbel-Max Causal MechanismsGuy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan MICo: Improved Representations via Sampling-Based State Similarity for Markov Decision ProcessesPablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland Near-Optimal Lower Bounds For Convex Optimization For All Orders of SmoothnessAnkit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif Neural Circuit Synthesis from Specification PatternsFrederik Schmitt, Christopher Hahn, Markus N. Rabe, Bernd Finkbeiner Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose EstimationJogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi, Pradyumna YM, Varun Jampani, Anirban Chakraborty, R. Venkatesh Babu Object-Aware Contrastive Learning for Debiased Scene RepresentationSangwoo Mo, Hyunwoo Kang, Kihyuk Soh, Chun-Liang Li, Jinwoo Shin On Density Estimation with Diffusion ModelsDiederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho On Margin-Based Cluster Recovery with Oracle QueriesMarco Bressan, Nicolo Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice On Model Calibration for Long-Tailed Object Detection and Instance SegmentationTai-Yu Pan, Cheng Zhang, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao Parallelizing Thompson SamplingAmin Karbasi, Vahab Mirrokni, Mohammad Shadravan Reverse-Complement Equivariant Networks for DNA SequencesVincent Mallet, Jean-Philippe Vert Revisiting ResNets: Improved Training and Scaling StrategiesIrwan Bello, William Fedus, Xianzhi Du, Ekin Dogus Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph Revisiting the Calibration of Modern Neural NetworksMatthias Minderer, Josip Djolonga, Rob Romijnders, Frances Ann Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic Scaling Vision with Sparse Mixture of ExpertsCarlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, Neil Houlsby SE(3)-Equivariant Prediction of Molecular Wavefunctions and Electronic DensitiesOliver Thorsten Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt, Klaus Robert Muller Stateful ODE-Nets Using Basis Function ExpansionsAlejandro Francisco Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney Statistically and Computationally Efficient Linear Meta-Representation LearningKiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh Streaming Belief Propagation for Community DetectionYuchen Wu, Jakab Tardos, Mohammad Hossein Bateni, André Linhares, Filipe Miguel Gonçalves de Almeida, Andrea Montanari, Ashkan Norouzi-Fard Synthetic Design: An Optimization Approach to Experimental Design with Synthetic ControlsNick Doudchenko, Khashayar Khosravi, Jean Pouget-Abadie, Sebastien Lahaie, Miles Lubin, Vahab Mirrokni, Jann Spiess, Guido Imbens The Difficulty of Passive Learning in Deep Reinforcement LearningGeorge Ostrovski, Pablo Samuel Castro, Will Dabney The Pareto Frontier of Model Selection for General Contextual BanditsTeodor Marinov, Julian Zimmert VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and TextHassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, Boqing Gong Co-Adaptation of Algorithmic and Implementational Innovations in Inference-Based Deep Reinforcement LearningHiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, Shixiang Gu Conservative Data Sharing for Multi-Task Offline Reinforcement LearningTianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn Does Knowledge Distillation Really Work?Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson Exponential Graph is Provably Efficient for Decentralized Deep TrainingBicheng Ying, Kun Yuan, Yiming Chen, Hanbin Hu, Pan Pan, Wotao Yin Faster Matchings via Learned DualsMichael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii Improved Transformer for High-Resolution GANsLong Zhao, Zizhao Zhang, Ting Chen, Dimitris N. Metaxas, Han Zhang Near-Optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical SystemsPrateek Jain, Suhas S. Kowshik, Dheeraj Mysore Nagaraj, Praneeth Netrapalli Nearly Horizon-Free Offline Reinforcement LearningTongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi Overparameterization Improves Robustness to Covariate Shift in High DimensionsNilesh Tripuraneni, Ben Adlam, Jeffrey Pennington Pay Attention to MLPsHanxiao Liu, Zihang Dai, David R. So, Quoc V. Le PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and RepairZimin Chen*, Vincent Josua Hellendoorn*, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra Prior-Independent Dynamic Auctions for a Value-Maximizing Buyer Yuan Deng, Hanrui Zhang Remember What You Want to Forget: Algorithms for Machine UnlearningAyush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh Reverse Engineering Learned Optimizers Reveals Known and Novel MechanismsNiru Maheswaranathan*, David Sussillo*, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein Revisiting 3D Object Detection From an Egocentric PerspectiveBoyang Deng, Charles R. Qi, Mahyar Najibi, Thomas Funkhouser, Yin Zhou, Dragomir Anguelov Robust Auction Design in the Auto-Bidding WorldSantiago Balseiro, Yuan Deng, Jieming Mao, Vahab Mirrokni, Song Zuo Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training DataQi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi Understanding How Encoder-Decoder Architectures AttendKyle Aitken, Vinay V. Ramasesh, Yuan Cao, Niru Maheswaranathan Understanding the Effect of Stochasticity in Policy Optimization Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvari, Dale Schuurmans Accurately Solving Rod Dynamics with Graph LearningHan Shao, Tassilo Kugelstadt, Torsten Hädrich, Wojtek Palubicki, Jan Bender, Sören Pirk, Dominik L. Michels GradInit: Learning to Initialize Neural Networks for Stable and Efficient TrainingChen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein Learnability of Linear Thresholds from Label ProportionsRishi Saket MLP-Mixer: An All-MLP Architecture for VisionIlya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy Neural Additive Models: Interpretable Machine Learning with Neural NetsRishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, Geoffrey Hinton Neural Production SystemsAnirudh Goyal, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio Physics-Aware Downsampling with Deep Learning for Scalable Flood ModelingNiv Giladi, Zvika Ben-Haim, Sella Nevo, Yossi Matias, Daniel Soudry Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving ObjectsDenys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Marc Pollefeys What Matters for Adversarial Imitation Learning?Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz A Convergence Analysis of Gradient Descent on Graph Neural NetworksPranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi A Geometric Analysis of Neural Collapse with Unconstrained FeaturesZhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu Agnostic Reinforcement Learning with Low-Rank MDPs and Rich ObservationsChristoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan Controlled Text Generation as Continuous Optimization with Multiple ConstraintsSachin Kumar, Eric Malmi, Aliaksei Severyn, Yulia Tsvetkov Coupled Gradient Estimators for Discrete Latent VariablesZhe Dong, Andriy Mnih, George Tucker Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-Training EnsemblesJiefeng Chen*, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, Somesh Jha Neural Active Learning with Performance GuaranteesZhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile Optimal Sketching for Trace EstimationShuli Jiang, Hai Pham, David Woodruff, Qiuyi (Richard) Zhang Representing Long-Range Context for Graph Neural Networks with Global AttentionZhanghao Wu, Paras Jain, Matthew A. Wright, Azalia Mirhoseini, Joseph E. Gonzalez, Ion Stoica Scaling Up Exact Neural Network Compression by ReLU StabilityThiago Serra, Xin Yu, Abhinav Kumar, Srikumar Ramalingam Soft Calibration Objectives for Neural NetworksArchit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael Curtis Mozer, Rebecca Roelofs Sub-Linear Memory: How to Make Performers SLiMValerii Likhosherstov, Krzysztof Choromanski, Jared Davis, Xingyou Song, Adrian Weller A New Theoretical Framework for Fast and Accurate Online Decision-MakingNicolò Cesa-Bianchi, Tommaso Cesari, Yishay Mansour, Vianney Perchet Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-LearningNan Ding, Xi Chen, Tomer Levinboim, Sebastian Goodman, Radu Soricut Differentially Private Multi-Armed Bandits in the Shuffle ModelJay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer Efficient and Local Parallel Random WalksMichael Kapralov, Silvio Lattanzi, Navid Nouri, Jakab Tardos Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference LossMichael Louis Iuzzolino, Michael Curtis Mozer, Samy Bengio* It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse ProblemsRegev Cohen, Yochai Blau, Daniel Freedman, Ehud Rivlin Learning to Combine Per-Example Solutions for Neural Program SynthesisDisha Shrivastava, Hugo Larochelle, Daniel Tarlow LLC: Accurate, Multi-purpose Learnt Low-Dimensional Binary CodesAditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham Kakade, Ali Farhadi There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning (see blog post)Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist A Near-Optimal Algorithm for Debiasing Trained Machine Learning ModelsIbrahim Alabdulmohsin, Mario Lucic Adaptive Sampling for Minimax Fair ClassificationShubhanshu Shekhar, Greg Fields, Mohammad Ghavamzadeh, Tara Javidi Asynchronous Stochastic Optimization Robust to Arbitrary DelaysAlon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain Boosting with Multiple SourcesCorinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha Suresh Breaking the Centralized Barrier for Cross-Device Federated LearningSai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stitch, Ananda Theertha Sureshi Canonical Capsules: Self-Supervised Capsules in Canonical PoseWeiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey Hinton, Kwang Moo Yi Contextual Recommendations and Low-Regret Cutting-Plane AlgorithmsSreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasi Manurangsi, Renato Paes Leme, Jon Schneider Decision Transformer: Reinforcement Learning via Sequence ModelingLili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee|Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch Deep Learning on a Data Diet: Finding Important Examples Early in TrainingMansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite Deep Learning with Label Differential PrivacyBadih Ghazi, Noah Golowich*, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang Efficient Training of Retrieval Models Using Negative CacheErik Lindgren, Sashank J. Reddi, Ruiqi Guo, Sanjiv Kumar Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video ParsingYan-Bo Lin, Hung-Yu Tseng, Hsin-Ying Lee, Yen-Yu Lin, Ming-Hsuan Yang Federated Reconstruction: Partially Local Federated LearningKaran Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, Sushant Prakash Framing RNN as a Kernel Method: A Neural ODE ApproachAdeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard Biau Learning Semantic Representations to Verify Hardware DesignsShobha Vasudevan, Wenjie Jiang, David Bieber, Rishabh Singh, Hamid Shojaei, C. Richard Ho, Charles Sutton Learning with User-Level PrivacyDaniel Asher Nathan Levy*, Ziteng Sun*, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh Logarithmic Regret from Sublinear HintsAditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit Margin-Independent Online Multiclass Learning via Convex GeometryGuru Guruganesh, Allen Liu, Jon Schneider, Joshua Ruizhi Wang Multiclass Boosting and the Cost of Weak LearningNataly Brukhim, Elad Hazan, Shay Moran, Indraneel Mukherjee, Robert E. Schapire Neural-PIL: Neural Pre-integrated Lighting for Reflectance DecompositionMark Boss, Varun Jampani, Raphael Braun, Ce Liu*, Jonathan T. Barron, Hendrik Lensch Never Go Full Batch (in Stochastic Convex Optimization)Idan Amir, Yair Carmon, Tomer Koren, Roi Livni On Large-Cohort Training for Federated LearningZachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith On the Sample Complexity of Privately Learning Axis-Aligned RectanglesMenachem Sadigurschi, Uri Stemmer Online Control of Unknown Time-Varying Dynamical SystemsEdgar Minasyan, Paula Gradu, Max Simchowitz, Elad Hazan Online Knapsack with Frequency PredictionsSungjin Im, Ravi Kumar,Mahshid Montazer Qaem, Manish Purohit Optimal Rates for Random Order Online OptimizationUri Sherman, Tomer Koren, Yishay Mansour Oracle-Efficient Regret Minimization in Factored MDPs with Unknown StructureAviv Rosenberg, Yishay Mansour Practical Large-Scale Linear Programming Using Primal-Dual Hybrid GradientDavid Applegate, Mateo Díaz*, Oliver Hinder, Haihao Lu*, Miles Lubin, Brendan O’Donoghue, Warren Schudy Private and Non-Private Uniformity Testing for Ranking DataRobert Istvan Busa-Fekete, Dimitris Fotakis, Manolis Zampetakis Privately Learning SubspacesVikrant Singhal, Thomas Steinke Provable Representation Learning for Imitation with Contrastive Fourier FeaturesOfir Nachum, Mengjiao Yang Safe Reinforcement Learning with Natural Language ConstraintsTsung-Yen Yang, Michael Hu, Yinlam Chow, Peter J. Ramadge, Karthik Narasimhan Searching for Efficient Transformers for Language ModelingDavid R. So, Wojciech Mańke, Hanxiao Liu, Zihang Dai, Noam Shazeer, Quoc V. Le SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic RegressionSteve Yadlowsky, Taedong Yun, Cory McLean, Alexander D’Amour Streaming Linear System Identification with Reverse Experience ReplayPrateek Jain, Suhas S. Kowshik, Dheeraj Mysore Nagaraj, Praneeth Netrapalli The Skellam Mechanism for Differentially Private Federated LearningNaman Agarwal, Peter Kairouz, Ziyu Liu* TokenLearner: Adaptive Space-Time Tokenization for VideosMichael S. Ryoo, AJ Piergiovanni, Anurag Arnab, Mostafa Dehghani, Anelia Angelova Towards Best-of-All-Worlds Online Learning with Feedback GraphsLiad Erez, Tomer Koren Training Over-Parameterized Models with Non-decomposable ObjectivesHarikrishna Narasimhan, Aditya Krishna Menon Twice Regularized MDPs and the Equivalence Between Robustness and RegularizationEsther Derman, Matthieu Geist, Shie Mannor Unsupervised Learning of Compositional Energy ConceptsYilun Du, Shuang Li, Yash Sharma, Joshua B. Tenenbaum, Igor Mordatch User-Level Differentially Private Learning via Correlated SamplingBadih Ghazi, Ravi Kumar, Pasin Manurangsi ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape ReconstructionGengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Ce Liu*, Deva Ramanan A Minimalist Approach to Offline Reinforcement LearningScott Fujimoto, Shixiang Gu A Unified View of cGANs With and Without ClassifiersSi-An Chen, Chun-Liang Li, Hsuan-Tien Lin CoAtNet: Marrying Convolution and Attention for All Data Sizes (see blog post)Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan Combiner: Full Attention Transformer with Sparse Computation CostHongyu Ren*, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai Contrastively Disentangled Sequential Variational AutoencoderJunwen Bai, Weiran Wang, Carla P. Gomes Controlling Neural Networks with Rule RepresentationsSungyong Seo, Sercan O. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister Dataset Distillation with Infinitely Wide Convolutional NetworksTimothy Nguyen*, Roman Novak, Lechao Xiao, Jaehoon Lee Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind ChessGregory Clark Differentially Private Learning with Adaptive ClippingGalen Andrew, Om Thakkar, Swaroop Ramaswamy, Hugh Brendan McMahan Differentially Private Model PersonalizationPrateek Jain, Keith Rush, Adam Smith, Shuang Song, Abhradeep Thakurta Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU ActivationsPranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan Efficiently Identifying Task Groupings for Multi-Task LearningChristopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn Generalized Shape Metrics on Neural RepresentationsAlex H. Williams, Erin Kunz, Simon Kornblith, Scott Linderman High-Probability Bounds for Non-Convex Stochastic Optimization with Heavy TailsAshok Cutkosky, Harsh Mehta Identity Testing for Mallows ModelRóbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi, Manolis Zampetakis Learnable Fourier Features for Multi-dimensional Spatial Positional EncodingYang Li, Si Si, Gang Li, Cho-Jui Hsieh, Samy Bengio* Learning to Select Exogenous Events for Marked Temporal Point ProcessPing Zhang, Rishabh K. Iyer, Ashish V. Tendulkar, Gaurav Aggarwal, Abir De Meta-learning to Improve Pre-trainingAniruddh Raghu, Jonathan Peter Lorraine, Simon Kornblith, Matthew B.A. McDermott, David Duvenaud Pointwise Bounds for Distribution Estimation Under Communication ConstraintsWei-Ning Chen, Peter Kairouz, Ayfer Özgür REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People Under Weak SupervisionMihai Fieraru, Mihai Zanfir, Teodor Alexandru Szente, Eduard Gabriel Bazavan, Vlad Olaru, Cristian Sminchisescu Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov Revealing and Protecting Labels in Distributed TrainingTrung Dang, Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, Peter Chin, Françoise Beaufays Robust Predictable ControlBenjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine Robust Visual Reasoning via Language Guided Neural Module NetworksArjun Reddy Akula, Varun Jampani, Soravit Changpinyo, Song-Chun Zhu Towards Understanding Retrosynthesis by Energy-Based ModelsRuoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai Exploring the Limits of Out-of-Distribution DetectionStanislav Fort, Jie Ren, Balaji Lakshminarayanan Minimax Regret for Stochastic Shortest PathAlon Cohen, Yonathan Efroni, Yishay Mansour, Aviv Rosenberg No Regrets for Learning the Prior in BanditsSoumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvari Structured Denoising Diffusion Models in Discrete State-SpacesJacob Austin, Daniel D. Johnsonv, Jonathan Ho, Daniel Tarlow, Rianne van den Berg The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning (see blog post)Yujin Tang, David Ha On the Existence of The Adversarial Bayes ClassifierPranjal Awasthi, Natalie Frank, Mehyrar Mohri Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement LearningChristopher Dann, Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement LearningChristopher Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert Datasets & Benchmarks Accepted PapersReduced, Reused and Recycled: The Life of a Dataset in Machine Learning ResearchBernard Koch, Emily Denton, Alex Hanna, Jacob G. FosterDatasets & Benchmarks Best Paper Constructing a Visual Dataset to Study the Effects of Spatial Apartheid in South AfricaRaesetje Sefala, Timnit Gebru, Luzango Mfupe, Nyalleng Moorosi AI and the Everything in the Whole Wide World BenchmarkInioluwa Deborah Raji, Emily M. Bender, Amandalynne Paullada, Emily Denton, Alex Hannah A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning ApproachesVincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, Hugo Larochelle The Neural MMO Platform for Massively Multi-agent ResearchJoseph Suarez, Yilun Du, Clare Zhu, Igor Mordatch, Phillip Isola Systematic Evaluation of Causal Discovery in Visual Model-Based Reinforcement LearningNan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajole, Stefan Bauer, Danilo Rezende, Yoshua Bengio, Michael Mozer, Christopher Pal STEP: Segmenting and Tracking Every PixelMark Weber, Jun Xie, Maxwell Collins, Yukun Zhu, Paul Voigtlaender, Hartwig Adam, Bradley Green, Andreas Geiger, Bastian Leibe, Daneil Cremers, Aljosa Osep, Laura Leal-Taixe, Liang-Chieh Chen Artsheets for Art DatasetsRamya Srinivisan, Emily Denton, Jordan Famularo, Negar Rostamzadeh, Fernando Diaz, Beth Coleman SynthBio: A Case in Human–AI Collaborative Curation of Text DatasetsAnn Yuan, Daphne Ippolito, Vitaly Niolaev, Chris Callison-Burch, Andy Coenen, Sebastian Gehrmann Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection TasksNeil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal Brax – A Differentiable Physics Engine for Large Scale Rigid Body Simulation (see blog post) C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem MLPerf Tiny BenchmarkColby Banbury, Vijay Janapa Reddi, Peter Torelli, Jeremy Holleman, Nat Jeffries, Csaba Kiraly, Pietro Montino, David Kanter, Sebastian Ahmed, Danilo Pau, Urmish Thakker, Antonio Torrini, Peter Warden, Jay Cordaro, Giuseppe Di Guglielmo, Javier Duarte, Stephen Gibellini, Videet Parekh, Honson Tran, Nhan Tran, Niu Wenxu, Xu Xuesong Automatic Construction of Evaluation Suites for Natural Language Generation DatasetsSimon Mille, Kaustubh D. Dhole, Saad Mahamood, Laura Perez-Beltrachini, Varun Gangal, Mihir Kale, Emiel van Miltenburg, Sebastian Gehrmann An Empirical Investigation of Representation Learning for ImitationXin Chen, Sam Toyer, Cody Wild, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven Wang, Ping Luo, Stuart Russell, Pieter Abbeel, Rohin Shah Multilingual Spoken Words CorpusMark Mazumder, Sharad Chitlangia, Colby Banbury, Yiping Kang, Juan Manuel Ciro, Keith Achorn, Daniel Galvez, Mark Sabini, Peter Mattson, David Kanter, Greg Diamos, Pete Warden, Josh Meyer, Vijay Janapa Reddi Workshops4th Robot Learning Workshop: Self-Supervised and Lifelong LearningSponsor: Google Organizers include Alex Bewley, Vincent Vanhoucke Differentiable Programming WorkshopSponsor: Google Machine Learning for Creativity and DesignSponsor: Google Organizers include: Daphne Ippolito, David Ha LatinX in AI (LXAI) Research @ NeurIPS 2021Sponsor: Google Sponsorship Level: Platinum Workshop Chairs include: Andres Munoz Medina Mentorship Roundtables include: Jonathan Huang, Pablo Samuel Castro Algorithmic Fairness Through the Lens of Causality and RobustnessOrganizers include: Jessica Schrouff, Awa Dieng ImageNet: Past, Present, and FutureOrganizers include: Lucas Beyer, Xiaohua ZhaiSpeakers include: Emily Denton, Vittorio Ferrari, Alex Hanna, Alex Kolesnikov, Rebecca Roelofs Optimal Transport and Machine LearningOrganizers include: Marco Cuturi Safe and Robust Control of Uncertain SystemsSpeakers include: Aleksandra Faust CtrlGen: Controllable Generative Modeling in Language and VisionSpeakers include: Sebastian Gehrmann Deep Reinforcement LearningOrganizers include: Chelsea FinnSpeakers include: Karol Hausam, Dale Schuurmans Distribution Shifts: Connecting Methods and Applications (DistShift)Speakers include: Chelsea Finn ML For SystemsOrganizers include: Anna Goldie, Martin Maas, Azade Nazi, Azalia Mihoseini, Milad Hashemi, Kevin Swersky Learning in Presence of Strategic BehaviorOrganizers include: Yishay Mansour Bayesian Deep LearningOrganizers include: Zoubin Ghahramani, Kevin Murphy Advances in Programming Languages and Neurosymbolic Systems (AIPLANS)Organizers include: Disha Shrivastava, Vaibhav Tulsyan, Danny Tarlow Ecological Theory of Reinforcement Learning: How Does Task Design Influence Agent Learning?Organizers include: Shixiang Shane Gu, Pablo Samuel Castro, Marc G. Bellemare The Symbiosis of Deep Learning and Differential EquationsOrganizers include: Lily Hu Out-of-Distribution Generalization and Adaptation in Natural and Artificial IntelligenceSpeakers include: Chelsea Finn Cooperative AIOrganizers include: Natasha Jaques Offline Reinforcement LearningOrganizers include: Rishabh Agarwal, George TuckerSpeakers include: Minmin Chen 2nd Workshop on Self-Supervised Learning: Theory and PracticeOrganizers include: Kristina Toutanova Data Centric AI Organizers include: Lora Aroyo Math AI for Education (MATHAI4ED): Bridging the Gap Between Research and Smart EducationOrganizers include: Yuhai (Tony) Wu TutorialsBeyond Fairness in Machine Learning Organizers include: Emily Denton CompetitionsEvaluating Approximate Inference in Bayesian Deep LearningOrganizers include: Matthew D. Hoffman, Sharad Vikram HEAR 2021 NeurIPS Challenge Holistic Evaluation of Audio RepresentationsOrganizers include: Jesse Engel Machine Learning for Combinatorial Optimization Organizers include: Pawel Lichocki, Miles Lubin *Work done while at Google.  ↩ †Currently at Google.  ↩

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