This week marks the beginning of the 36th annual Conference on Neural Information Processing Systems (NeurIPS 2022), the biggest machine learning conference of the year, which is being held in New Orleans, LA. NeurIPS 2022 will be held in person with additional options for virtual attendees, and includes invited talks, demonstrations and presentations of some of the latest in machine learning research. This year, NeurIPS is also offering a new track, called Spotlight Papers, which will provide opportunities to highlight papers presented in prestigious journals that would otherwise not have been eligible for submission.
Google is proud to be a Diamond level sponsor of NeurIPS this year and will have a significant presence year with more than 175 accepted papers, additionally contributing to and learning from the broader academic research community through numerous 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
General Chairs includes: Sanmi Koyejo
Program Chairs include: Alekh Agarwal
Workshop Chairs include: Hanie Sedghi
Tutorial Chairs include: Adji Bousso Dieng, Jessica Schrouff
Affinity Workshop Chair: Adji Bousso Dieng, Jessica Schrouff
Program Committee, Senior Area Chairs include: Corinna Cortes, Claudio Gentile, Mohammad Ghavamzadeh, Amir Globerson, Elad Hazan, Katherine Heller, Satyen Kale, Been Kim, Sanjiv Kumar, Hugo Larochelle, Sergey Levine, Yishay Mansour, Mehryar Mohri, Tara Sainath, Dale Schuurmans, Daniel Tarlow
NeurIPS Foundation Board Secretary: Michael Mozer
NeurIPS Foundation Board Members include: Corinna Cortes, Isabelle Guyon, Sanmi Koyejo, Hugo Larochelle
NeurIPS Foundation Advisory Board include: Peter Bartlett, Zoubin Ghahramani, John C. Platt, Fernando Pereira, Dale Schuurmans
Keynote Speakers
The Data-Centric Era: How ML is Becoming an Experimental Science
Isabelle Guyon
The Forward-Forward Algorithm for Training Deep Neural Networks
Geoffrey Hinton
Outstanding Paper Award
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, Mohammad Norouzi
EXPO Day Workshops
Graph Neural Networks in Tensorflow: A Practical Guide
Workshop Organizers include: Bryan Perozzi, Sami Abu-el-Haija
A Hands-On Introduction to Tensorflow and Jax
Workshop Organizers include: Josh Gordon
Affinity Workshops
LatinX in AI (LXAI)
Platinum Sponsor
Networking & Social Chairs include: Andres Muñoz Medina
Program Committee includes: Johan Obando Ceron
Queer in AI
Panelists include: Sara Beery, Talia Ringer
Women in Machine Learning (WiML)
Platinum Sponsor
Workshop Organizers and Mentorship Chairs include: Beliz Gunel
Mentors include: Adam Roberts, Eleni Triantafillou, Zelda Mariet, Clara Hu, Rosanne Liu, Alekh Agarwal, Vinod Prabhakaran, Rose Yu, Katherine Heller
Workshops
New in ML
Workshop Organizers include: Isabelle Guyon
AI for Accelerated Materials Design (AI4Mat)
Workshop Organizers include: Benjamin Sanchez-Lengeling
All Things Attention: Bridging Different Perspectives on Attention
Invited Speakers and Panelists include: Vidhya Navalpakkam
Efficient Natural Language and Speech Processing (ENLSP-II): The Future of Pre-trained Models
Invited Speakers include: Tara Sainath, Anna Huang
Invited Panelists include: Mohammad Norouzi
Program Committee includes: Wenhu Chen
Federated Learning: Recent Advances and New Challenges
Program Committee includes: Kallista Bonawitz, Zachary Charles, Wenshuo Guo, Peter Kairouz, Zhaozhuo Xu, Zheng Xu
Gaussian Processes, Spatiotemporal Modeling, and Decision-Making Systems
Workshop Organizers include: Zi Wang
Invited Speakers include: Jasper Snoek, Carolina Osorio
Advisory Board includes: Zoubin Ghahramani
Has it Trained Yet? A Workshop for Algorithmic Efficiency in Practical Neural Network Training
Workshop Organizers include: Zachary Nado, George Dahl, Naman Agarwal, Aakanksha Chowdhery
Invited Speakers include: Aakanksha Chowdhery, Priya Goyal
Human in the Loop Learning (HiLL)
Workshop Organizers include: Fisher Yu, Vittorio Ferrari
Invited Speakers include: Dorsa Singh, Igor Mordatch, Ding Zhao
INTERPOLATE — First Workshop on Interpolation Regularizers and Beyond
Workshop Organizers include: Yann Dauphin
Invited Speakers include: Chelsea Finn
Panelists include: Chelsea Finn, Dustin Tran
Program Committee includes: Wang Chen, Kimin Lee
LaReL: Language and Reinforcement Learning
Invited Speakers include: Dorsa Singh, Igor Mordatch
Medical Imaging Meets NeurIPS
Program Committee includes: Chenyu You
Memory in Artificial and Real Intelligence (MemARI)
Program Committee includes: Benjamin Eysenbach, Otilia Stretcu
Meta-Learning
Workshop Organizers include: Eleni Triantafillou
Invited Speakers include: Lucas Byer, Chelsea Finn
Program Committee includes: Ishita Dasgupta, Praneet Dutta, Benjamin Eysenbach, Maximilian Igl, Louis Kirsch, Parsa Mahmoudieh, Marc Pickett, Eleni Triantafillou
New Frontiers in Graph Learning (GLFrontiers)
Workshop Organizers include: Hanjun Dai
Offline Reinforcement Learning Workshop: Offline RL as a “Launchpad”
Workshop Organizers include: Rishabh Agarwal, Aviral Kumar, George Tucker
Invited Speakers include: Dorsa Sadigh
Score-Based Methods
Invited Speakers include: Mohammad Norouzi
Invited Panelists include: Jascha Sohl-Dickstein
Synthetic Data for Empowering ML Research
Invited Speakers include: Mehryar Mohri
Invited Panelists include: Katrina Ligett
Program Committee includes: Jinsung Yoon
Table Representation Learning
Workshop Organizers include: Pengcheng Yin
Invited Speakers include: Xinyun Chen, Carsten Binnig
Panelists include: Julian Eisenschlos
Program Committee includes: Wenhu Chen, Xinyun Chen, Beliz Gunel
A Causal View on Dynamical Systems
Program Committee includes: Rose Yu
Algorithmic Fairness Through the Lens of Causality and Privacy
Workshop Organizers include: Awa Dieng
Invited Speakers include: Nicolas Papernot
Roundtable Leads include: David Madras, Negar Rostamzadeh, Nyalleng Moroosi
Program Committee includes: Matt Kusner
Broadening Research Collaborations in ML
Workshop Organizers include: Rosanne Liu, Pablo Samuel Castro, Sunipa Dev
Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications
Invited Speakers include: Peter Kairouz
Distribution Shifts (DistShift): Connecting Methods and Applications
Workshop Organizers include: Becca Roelofs, Chelsea Finn, Jacob Eisenstein, Pang Wei Koh
Invited Speakers include: Sarah Beery
Foundation Models for Decision Making
Workshop Organizers include: Sherry Yang, Yilun Du, Igor Mordatch, Shixiang Shane Gu,Ofir Nachum
Invited Speakers include: Dorsa Sadigh, Dale Schuurmans, Machel Reid
Program Committee includes: Bo Dai, Aleksandra Faust, Hiroki Furuta, Kati Goshvadi, Izzeddin Gur, Austin Huang, Kimin Lee, Kuang-Huei Lee, Lisa Lee, Yingjie Miao, Jordi Orbay, Ted Xiao
Gaze Meets ML
Program Committee includes: Peter Mattson, Mehdi Moradi
I Can’t Believe It’s Not Better: Understanding Deep Learning Through Empirical Falsification
Workshop Organizers include: Javier Antorán
Panelists include: Kevin Murphy
Interactive Learning for Natural Language Processing
Invited Speakers include: Anca Dragan
Program Committees include: Julia Kreutzer, Shunyu Yao
Machine Learning and the Physical Sciences
Workshop Organizers include: Adji Bousso Dieng
Invited Speakers include: Ekin Doğuş Çubuk
Machine Learning for Systems
Workshop Organizers include: Martin Maas, Azade Nova, Dan Zhang
Invited Speakers include: Jeff Dean
Program Committee includes: Milad Hashemi, Kevin Swersky
Machine Learning in Structural Biology
Invited Speakers include: David Fleet
MATH-AI: Toward Human-Level Mathematical Reasoning
Workshop Organizers include: Swaroop Mishra, Yuhuai Wu
Invited Speakers include: Talia Ringer
OPT 2022: Optimization for Machine Learning
Workshop Organizers include: Courtney Paquette
Reinforcement Learning for Real Life (RL4RealLife)
Workshop Organizers include: Minmin Chen
Invited Panelists include: Pablo Samuel Castro
Program Committee includes: Victor Carbune, Bo Chang, Yinlam Chow, Konstantina Christakopoulou, Bo Dai, Hanjun Dai, Aleksandra Faust, Joshua Greaves, Chih-wei Hsu, Rahul Kidambi, Srivatsan Krishnan, Iou-Jen Liu, Cong Lu, Jincheng Mei, Chao Qin
Self-Supervised Learning – Theory and Practice
Invited Speakers include: Mathilde Caron
Symmetry and Geometry in Neural Representations (NeurReps)
Invited Speakers include: Noah Shutty
Program Committee includes: Ondrej Biza, Noah Shutty
Temporal Graph Learning Workshop
Invited Speakers include: Mehran Kazemi
Transfer Learning for Natural Language Processing
Workshop Organizers include: Deepak Ramachandran, Sebastian Ruder
Invited Speakers include: Jonas Pfeiffer
Invited Debaters include: Ellie Pavlick
Program Committee includes: Patrick Fernandes, Jonas Pfeiffer, Jiao Sun, Tu Vu, Xinyi Wang, Xin Xu
Cultures of AI and AI for Culture
Workshop Organizers include: Rida Qadri, Fernando Diaz
Deep Reinforcement Learning Workshop
Workshop Organizers include: Karol Hausman, Ted Xiao, Zeyu Zheng
Invited Speakers include: Igor Mordatch
Advisory Board includes: Chelsea Finn
Empowering Communities: A Participatory Approach to AI for Mental Health
Program Committee includes: Diana Mincu, Subhrajit Roy, Martin Seneviratne
HCAI@NeurIPS 2022, Human Centered AI
Keynote Speaker includes: Fernanda Viegas
Learning Meaningful Representations of Life
Workshop Organizers include: Adji Bousso Dieng
Machine Learning for Creativity and Design
Workshop Organizers include: Yingtao Tian
Machine Learning Safety
Workshop Organizers include: Nicholas Carlini
Invited Speakers include: Dorsa Sadigh
Neuro Causal and Symbolic AI (nCSI)
Workshop Organizers include: Thomas Kipf
Robot Learning Workshop: Trustworthy Robotics
Workshop Organizers include: Alex Bewley, Jonathan Tompson
Invited Speakers include: Karol Hausman, Brian Ichter, Been Kim, Leila Takayama, Andy Zeng
Program Committee includes: Vincent Vanhoucke
The Symbiosis of Deep Learning and Differential Equations II
Workshop Organizers include: Winnie Xu
Invited Speakers include: Rose Yu
Tackling Climate Change with Machine Learning
Workshop Organizers include: Emma Strubell
Trustworthy and Socially Responsible Machine Learning
Invited Speakers include: Been Kim, Dorsa Sadigh, Milind Tambe
Vision Transformers: Theory and Applications
Invited Speakers include: Cordelia Schmid, Ming Hsuan Yang
Tutorials
Advances in Bayesian Optimization
Tutorial Organizers include: Virginia Aglietti
Creative Culture and Machine Learning
Tutorial Organizers include: Negar Rostamzadeh
Fair and Socially Responsible ML for Recommendations: Challenges and Perspectives
Invited Panelists include: Fernando Diaz
Lifelong Learning Machines
Invited Panelists include: Christopher Summerfield
The Role of Meta-learning for Few-Shot Learning
Tutorial Organizers include: Eleni Triantafillou
Invited Panelists include: Neil Houlsby, Priyanka Agrawal
Competitions
NeurIPS 2022 Competition Track: Overview & Results
Invited Speakers include: Isabelle Guyon
Causal Insights for Learning Paths in Education
Competition Organizers include: Zichao (Jack) Wang
IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
Competition Organizers include: Negar Arabzadeh
Cross-Domain MetaDL: Any-Way Any-Shot Learning Competition with Novel Datasets from Practical Domains
Competition Organizers include: Isabelle Guyon
Reconnaissance Blind Chess: An Unsolved Challenge for Multi-Agent Decision Making Under Uncertainty
Competition Organizers include: Bo Li
VisDA 2022 Challenge: Sim2Real Domain Adaptation for Industrial Recycling
Competition Organizers include: Dina Bashkirova
Spotlight Papers
CoPur: Certifiably Robust Collaborative Inference via Feature Purification
Jing Liu, Chulin Xie, Oluwasanmi O Koyejo, Bo Li
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
Ines Chami*, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy
Sparse Winning Tickets are Data-Efficient Image Recognizers
Mukund Varma T, Xuxi Chen, Zhenyu Zhang, Tianlong Chen, Subhashini Venugopalan, Zhangyang Wang
Federated Learning from Pre-trained Models: A Contrastive Learning Approach
Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, Jing Jiang
Improving Multi-task Generalization via Regularizing Spurious Correlation
Ziniu Hu*, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, Ed H. Chi
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning
Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
Residual Multiplicative Filter Networks for Multiscale Reconstruction
Shayan Shekarforoush, David B. Lindell, David J. Fleet, Marcus A Brubaker
Differentially Private Learning with Margin Guarantees
Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
Optimal Query Complexities for Dynamic Trace Estimation
David P. Woodruff*, Fred Zhang*, Qiuyi Zhang
Papers
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
Ayush Sekhari, Satyen Kale, Jason D. Lee, Chris De Sa, Karthik Sridharan
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games
Runyu Zhang, Jincheng Mei, Bo Dai, Dale Schuurmans, Na Li
Matryoshka Representation Learning
Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham Kakade, Prateek Jain, Ali Farhadi
Efficient Risk-Averse Reinforcement Learning
Ido Greenberg, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor
Operator Splitting Value Iteration
Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-massoud Farahmand
Cluster Randomized Designs for One-Sided Bipartite Experiments
Jennifer Brennan*, Vahab Mirrokni, Jean Pouget-Abadie
A Unified Sequence Interface for Vision Tasks
Ting Chen, Saurabh Saxena, Lala Li, Tsung-Yi Lin*, David J. Fleet, Geoffrey Hinton
Cryptographic Hardness of Learning Halfspaces with Massart Noise
Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi, Lisheng Ren
Better Best of Both Worlds Bounds for Bandits with Switching Costs
Idan Amir, Guy Azov, Tomer Koren, Roi Livni
Fast Neural Kernel Embeddings for General Activations
Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth
Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirronki, Jessica Shi
Improving Zero-Shot Generalization in Offline Reinforcement Learning Using Generalized Similarity Functions
Bogdan Mazoure*, Ilya Kostrikov, Ofir Nachum, Jonathan Tompson
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
Maura Pintor, Luca Demetrio, Angelo Sotgiu, Ambra Demontis, Nicholas Carlini, Battista Biggio, Fabio Roli
Learning Energy Networks with Generalized Fenchel-Young Losses
Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist
Learning Robust Dynamics Through Variational Sparse Gating
Arnav Kumar Jain, Shiva Kanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, Samira Ebrahimi Kahou
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures
Arnav Kumar Jain, Shiva Kanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, Samira Ebrahimi Kahou
So3krates: Equivariant Attention for Interactions on Arbitrary Length-Scales in Molecular Systems
J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller
Spectral Bias in Practice: The Role of Function Frequency in Generalization
Sara Fridovich-Keil*, Raphael Gontijo-Lopes, Rebecca Roelofs
Delving into Out-of-Distribution Detection with Vision-Language Representations
Yifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun, Wei Li, Yixuan Li
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation
Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, J. Zico Kolter, Roger Grosse
On Optimal Learning Under Targeted Data Poisoning
Steve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran
Learning With Little Mixing
Ingvar Ziemann, Stephen Tu
Block-Recurrent Transformers
DeLesley Hutchins, Imanol Schlag*, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
Chengrun Yang, Gabriel Bender, Hanxiao Liu, Pieter-Jan Kindermans, Madeleine Udell, Yifeng Lu, Quoc Le, Da Huang
Regret Bounds for Multilabel Classification in Sparse Label Regimes
Robert Busa-Fekete, Heejin Choi, Krzysztof Dembczynski, Claudio Gentile, Henry William Reeve, Balazs Szorenyi
Robust Reinforcement Learning Using Offline Data
Kishan Panaganti, Zaiyan Xu, Dileep Kalathil, Mohammad Ghavamzadeh
Contrastive Learning as Goal-Conditioned Reinforcement Learning
Benjamin Eysenbach, Tianjun Zhang, Sergey Levine, Ruslan Salakhutdinov
Beyond Rewards: A Hierarchical Perspective on Offline Multiagent Behavioral Analysis
Shayegan Omidshafiei, Andrei Kapishnikov, Yannick Assogba, Lucas Dixon, Been Kim
Revisiting Neural Scaling Laws in Language and Vision
Ibrahim Alabdulmohsin, Behnam Neyshabur, Xiaohua Zhai
Polynomial Neural Fields for Subband Decomposition and Manipulation
Guandao Yang*, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan T. Barron, Thomas Funkhouser, Bharath Hariharan, Serge Belongie
First Is Better Than Last for Language Data Influence
Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar
The Privacy Onion Effect: Memorization Is Relative
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramer
Deep Hierarchical Planning from Pixels (see blog post)
Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel
Discovered Policy Optimisation
Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schroeder de Witt, Jakob Foerster
Semi-supervised Active Linear Regression
Fnu Devvrit, Nived Rajaraman, Pranjal Awasthi
Pruning’s Effect on Generalization Through the Lens of Training and Regularization
Tian Jin, Daniel M. Roy, Michael Carbin, Jonathan Frankle, Gintare Karolina Dziugaite
Exploring Length Generalization in Large Language Models
Cem Anil*, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm Under Parallelization
Benjamin Dubois-Taine, Francis Bach, Quentin Berthet, Adrien Taylor
Global Normalization for Streaming Speech Recognition in a Modular Framework
Ehsan Variani, Ke Wu, Michael Riley, David Rybach, Matt Shannon, Cyril Allauzen
Learning Predictions for Algorithms with Predictions
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts (see blog post)
Basil Mustafa, Carlos Riquelme, Joan Puigcerver, Rodolphe Jenatton, Neil Houlsby
Incrementality Bidding via Reinforcement Learning Under Mixed and Delayed Rewards
Ashwinkumar Badanidiyuru, Zhe Feng, Tianxi Li, Haifeng Xu*
Solving Quantitative Reasoning Problems with Language Models (see blog post)
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra
Anonymized Histograms in Intermediate Privacy Models
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Efficient and Stable Fully Dynamic Facility Location
Sayan Bhattacharya, Nikos Parotsidis, Silvio Lattanzi
Are All Losses Created Equal: A Neural Collapse Perspective
Jinxin Zhou, Chong You, Xiao Li, Kangning Liu, Sheng Liu, Qing Qu, Zhihui Zhu
Universal Rates for Interactive Learning
Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions
Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu
Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes
Alkis Kalavasis, Grigoris Velegkas, Amin Karbasi
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning
Cenk Baykal, Nishanth Dikkala, Rina Panigrahy, Cyrus Rashtchian, Xin Wang
Pre-trained Language Models for Interactive Decision-Making
Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, Jacob Andreas, Igor Mordatch, Antonio Torralba, Yuke Zhu
Polynomial Neural Fields for Subband Decomposition and Manipulation
Guandao Yang*, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan T. Barron, Thomas Funkhouser, Bharath Hariharan, Serge Belongie
Submodular Maximization in Clean Linear Time
Wenxin Li, Moran Feldman, Ehsan Kazemi, Amin Karbasi
Reinforcement Learning with Logarithmic Regret and Policy Switches
Grigoris Velegkas, Zhuoran Yang, Amin Karbasi
Algorithms with Prediction Portfolios
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii
Understanding and Improving Robustness of Vision Transformers Through Patch-Based Negative Augmentation
Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang
Best of Both Worlds Model Selection
Aldo Pacchiano, Christoph Dann, Claudio Gentile
Fair Wrapping for Black-Box Predictions
Alexander Soen, Ibrahim Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie
A Reduction to Binary Approach for Debiasing Multiclass Datasets
Ibrahim Alabdulmohsin, Jessica Schrouff, Oluwasanmi Koyejo
Weighted Distillation with Unlabeled Examples
Fotis Iliopoulos, Vasilis Kontonis, Cenk Baykal, Gaurav Menghani, Khoa Trihn,Erik Vee
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases
James Harrison, Luke Metz, Jascha Sohl-Dickstein
Post-hoc Estimators for Learning to Defer to an Expert
Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar
Model-Based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity
Alekh Agarwal, Tong Zhang
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
Towards Learning Universal Hyperparameter Optimizers with Transformers (see blog post)
Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Qiuyi Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc’aurelio Ranzato, Sagi Perel, Nando de Freitas
Reproducibility in Optimization: Theoretical Framework and Limits
Kwangjun Ahn*, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
Confident Adaptive Language Modeling
Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Q. Tran, Yi Tay, Donald Metzler
Reinforcement Learning with Neural Radiance Fields
Danny Driess, Ingmar Schubert, Pete Florence, Yunzhu Li, Marc Toussaint
Invariant and Transportable Representations for Anti-Causal Domain Shifts
Yibo Jiang, Victor Veitch
Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions
Gagan Aggarwal, Kshipra Bhawalkar, Aranyak Mehta, Divyarthi Mohan, Alexandros Psomas
STaR: Bootstrapping Reasoning with Reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah D. Goodman
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality
Teodor V. Marinov, Mehryar Mohri, Julian Zimmert
The Curse of Unrolling: Rate of Differentiating Through Optimization
Damien Scieur, Quentin Bertrand, Gauthier Gidel, Fabian Pedregosa
Visual Prompting via Image Inpainting
Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, Alexei A Efros
Multi-Class H-Consistency Bounds
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
Anonymous Bandits for Multi-User Systems
Hossein Esfandiari, Vahab Mirrokni, Jon Schneider
Understanding the Eluder Dimension
Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro
Why So Pessimistic? Estimating Uncertainties for Offline RL Through Ensembles, and Why Their Independence Matters
Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum
A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback
Saeed Masoudian, Julian Zimmert, Yevgeny Seldin
A Theoretical View on Sparsely Activated Networks
Cenk Baykal, Nishanth Dikkala, Rina Panigrahy, Cyrus Rashtchian, Xin Wang
Chain of Thought Prompting Elicits Reasoning in Large Language Models (see blog post)
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou
Decoupled Context Processing for Context Augmented Language Modeling
Zonglin Li, Ruiqi Guo, Sanjiv Kumar
Exploring Through Random Curiosity with General Value Functions
Aditya Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber
Object Scene Representation Transformer
Mehdi S. M. Sajjadi, Daniel Duckworth, Aravindh Mahendran, Sjoerd van Steenkiste, Filip Pavetić, Mario Lučić, Leonidas J. Guibas, Klaus Greff, Thomas Kipf
Joint Model-Policy Optimization of a Lower Bound for Model-Based RL
Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov
A Fourier Approach to Mixture Learning
Mingda Qiao*, Guru Guruganesh, Ankit Singh Rawat, Avinava Dubey, Manzil Zaheer
Why Neural Networks Find Simple Solutions: The Many Regularizers of Geometric Complexity
Benoit Dherin, Michael Munn, Mihaela Rosca, David Barrett
Do Current Multi-task Optimization Methods in Deep Learning Even Help?
Derrick Xin, Behrooz Ghorbani, Ankush Garg, Orhan Firat, Justin Gilmer
Associating Objects and Their Effects in Video Through Coordination Games
Erika Lu, Forrester Cole, Weidi Xie, Tali Dekel, William Freeman, Andrew Zisserman, Michael Rubinstein
Increasing Confidence in Adversarial Robustness Evaluations
Roland S. Zimmermann*, Wieland Brendel, Florian Tramèr, Nicholas Carlini
The Role of Baselines in Policy Gradient Optimization
Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvari, Dale Schuurmans
Scaling Multimodal Pre-training via Cross-Modality Gradient Harmonization
Junru Wu, Yi Liang, Feng Han, Hassan Akbari, Zhangyang Wang, Cong Yu*
S3GC: Scalable Self-Supervised Graph Clustering
Fnu Devvrit*, Aditya Sinha, Inderjit Dhillon, Prateek Jain
Algorithms and Hardness for Learning Linear Thresholds from Label Proportions
Rishi Saket
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks
Shariq Iqbal, Robby Costales, Fei Sha
DC-BENCH: Dataset Condensation Benchmark
Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh
Does GNN Pre-training Help Molecular Representation?
Ruoxi Sun, Hanjun Dai, Adams Yu
Drawing Out of Distribution with Neuro-Symbolic Generative Models
Yichao Liang, Joshua B. Tenenbaum, Tuan Anh Le, N. Siddharth
Mixture-of-Experts with Expert Choice Routing (see blog post)
Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew Dai, Zhifeng Chen, Quoc Le, James Laudon
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback
Tiancheng Jin, Tal Lancewicki, Haipeng Luo, Yishay Mansour, Aviv Rosenberg
Precise Learning Curves and Higher-Order Scalings for Dot-Product Kernel Regression
Lechao Xiao, Jeffrey Pennington, Theodor Misiakiewicz, Hong Hu, Yue Lu
Rate-Optimal Online Convex Optimization in Adaptive Linear Control
Asaf Cassel, Alon Cohen, Tomer Koren
Why Neural Networks Find Simple Solutions: The Many Regularizers of Geometric Complexity
Benoit Dherin, Michael Munn, Mihaela Rosca, David G.T. Barrett
Private Isotonic Regression
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Sketching Based Representations for Robust Image Classification with Provable Guarantees
Nishanth Dikkala, Sankeerth Rao Karingula, Raghu Meka, Jelani Nelson, Rina Panigrahy, Xin Wang
The Role of Baselines in Policy Gradient Optimization
Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvari, Dale Schuurmans
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
Elad Ben Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson
Near-Optimal Private and Scalable k-Clustering
Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan*, Peilin Zhong
When Does Differentially Private Learning Not Suffer in High Dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A Inan, Janardhan Kulkarni, YinTat Lee, Abhradeep Guha Thakurta
End-to-End Learning to Index and Search in Large Output Spaces
Nilesh Gupta, Patrick H. Chen, Hsiang-Fu, Yu, Cho-Jui Hsieh, Inderjit S. Dhillon
A Boosting Approach to Reinforcement Learning
Nataly Brukhim, Elad Hazan, Karan Singh
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction
Samiul Alam, Luyang Liu, Ming Yan, Mi Zhang
Non-Convex Online Learning via Algorithmic Equivalence
Udaya Ghai, Zhou Lu, Elad Hazan
Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations
Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Karthikeyan Shanmugam
SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos
Gamaleldin F. Elsayed, Aravindh Mahendran, Sjoerd van Steenkiste, Klaus Greff, Michael C. Mozer, Thomas Kipf
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes
Alexander Kolesnikov, André Susano Pinto, Lucas Beyer, Xiaohua Zhai, Jeremiah Harmsen, Neil Houlsby
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions
Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington
Multi-game Decision Transformers (see blog post)
Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski, Igor Mordatch
Subsidiary Prototype Alignment for Universal Domain Adaptation
Jogendra Nath Kundu, Suvaansh Bhambri, Akshay Ravindra Kulkarni, Hiran Sarkar, Varun Jampani, Venkatesh Babu Radhakrishnan
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
Mark Boss*, Andreas Engelhardt*, Abhishek Kar, Yuanzhen Li, Deqing Sun, Jonathan T. Barron, Hendrik P. A. Lensch, Varun Jampani
Chefs’ Random Tables: Non-Trigonometric Random Features
Valerii Likhosherstov, Krzysztof Marcin Choromanski, Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks
Mansheej Paul, Brett W Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite
DP-PCA: Statistically Optimal and Differentially Private PCA
Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh
Emergent Communication: Generalization and Overfitting in Lewis Games
Mathieu Rita, Corentin Tallec, Paul Michel, Jean-Bastien Grill, Olivier Pietquin, Emmanuel Dupoux, Florian Strub
Handcrafted Backdoors in Deep Neural Networks
Sanghyun Hong, Nicholas Carlini, Alexey Kurakin
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification
Muhammad Ferjad Naeem, Yongqin Xian, Luc Van Gool, Federico Tombari
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams
Sergey Denisov, Brendan McMahan, Keith Rush, Adam Smith, Abhradeep Guha Thakurta
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces
Haoran Sun*, Hanjun Dai, Dale Schuurmans
Near-Optimal Correlation Clustering with Privacy
Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers
Albert Q. Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygóźdź, Piotr Miłoś, Yuhuai Wu, Mateja Jamnik
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s
Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer, Sanjiv Kumar
When Does Dough Become a Bagel? Analyzing the Remaining Mistakes on ImageNet
Vijay Vasudevan, Benjamin Caine, Raphael Gontijo-Lopes, Sara Fridovich-Keil, Rebecca Roelofs
DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning
Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar
A Characterization of Semi-Supervised Adversarially Robust PAC Learnability
Idan Attias, Steve Hanneke, Yishay Mansour
Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation
Ziyu Jiang, Xuxi Chen, Xueqin Huang, Xianzhi Du, Denny Zhou, Zhangyang Wang
Subquadratic Kronecker Regression with Applications to Tensor Decomposition
Matthew Fahrbach, Gang Fu, Mehrdad Ghadiri
Zero-Shot Transfer Learning Within a Heterogeneous Graph via Knowledge Transfer Networks
Minji Yoon*, John Palowitch, Dustin Zelle, Ziniu Hu*, Ruslan Salakhutdinov, Bryan Perozzi
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank
Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress (see blog post)
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, Marc G. Bellemare
Private and Communication-Efficient Algorithms for Entropy Estimation
Gecia Bravo-Hermsdorff, Robert Busa-Fekete, Mohammad Ghavamzadeh, Andres Munoz Medina, Umar Syed
Oracle Inequalities for Model Selection in Offline Reinforcement Learning
Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill
Diagnosing Failures of Fairness Transfer Across Distribution Shift in Real-World Medical Settings
Jessica Schrouff*, Natalie Harris, Oluwasanmi O 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 A Heller, Silvia Chiappa, Alexander D’Amour
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
Patching Open-Vocabulary Models by Interpolating Weights
Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, Ludwig Schmidt
TUSK: Task-Agnostic Unsupervised Keypoints
Yuhe Jin, Weiwei Sun, Jan Hosang, Eduard Trulls, Kwang Moo Yi
Active Learning of Classifiers with Label and Seed Queries
Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice, Maximilian Thiessen
Autoformalization with Large Language Models
Yuhuai Wu, Albert Q. Jiang, Wenda Li, Markus N. Rabe, Charles Staats, Mateja Jamnik, Christian Szegedy
Benign Underfitting of Stochastic Gradient Descent
Tomer Koren, Roi Livni, Yishay Mansour, Uri Sherman
Chain of Thought Imitation with Procedure Cloning
Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum
Efficient and Modular Implicit Differentiation
Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert
Insights into Pre-training via Simpler Synthetic Tasks
Yuhuai Wu, Felix Li, Percy Liang
Self-Supervised Learning with an Information Maximization Criterion
Serdar Ozsoy, Shadi Hamdan, Sercan Ö. Arik, Deniz Yuret, Alper T. Erdogan
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model
Weihao Kong, Rajat Sen, Pranjal Awasthi, Abhimanyu Das
Using Embeddings for Causal Estimation of Peer Influence in Social Networks
Irina Cristali, Victor Veitch
VCT: A Video Compression Transformer
Fabian Mentzer, George Toderici, David Minnen, Sung-Jin Hwang, Sergi Caelles, Mario Lucic, Eirikur Agustsson
Video Diffusion Models
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, David J. Fleet
Large Language Models are Zero-Shot Reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa
Improved Coresets for Euclidean k-Means
Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar
On the Adversarial Robustness of Mixture of Experts
Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme Ruiz, Pranjal Awasthi, Srinadh Bhojanapalli
Stars: Tera-Scale Graph Building for Clustering and Learning
CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong
VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement
Erik Wijmans, Irfan Essa, Dhruv Batra
TaSIL: Taylor Series Imitation Learning
Daniel Pfrommer, Thomas TCK Zhang, Stephen Tu, Nikolai Matni
RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks
Leo Kozachkov, Michaela M Ennis, Jean-Jacques Slotine
Integral Probability Metrics PAC-Bayes Bounds
Ron Amit, Baruch Epstein, Shay Moran, Ron Meir
D2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video
Tianhao Wu, Fangcheng Zhong, Andrea Tagliasacchi, Forrester Cole, Cengiz Oztireli
Posted Pricing and Dynamic Prior-Independent Mechanisms with Value Maximizers
Yuan Deng, Vahab Mirrokni, Hanrui Zhang
Transformer Memory as a Differentiable Search Index
Yi Tay, Vinh Q. Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, Tal Schuster, William W. Cohen, Donald Metzler
Posted by Cat Armato, Program Manager, Google This week marks the beginning of the 36th annual Conference on Neural Information Processing Systems (NeurIPS 2022), the biggest machine learning conference of the year, which is being held in New Orleans, LA. NeurIPS 2022 will be held in person with additional options for virtual attendees, and includes invited talks, demonstrations and presentations of some of the latest in machine learning research. This year, NeurIPS is also offering a new track, called Spotlight Papers, which will provide opportunities to highlight papers presented in prestigious journals that would otherwise not have been eligible for submission. Google is proud to be a Diamond level sponsor of NeurIPS this year and will have a significant presence year with more than 175 accepted papers, additionally contributing to and learning from the broader academic research community through numerous 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 General Chairs includes: Sanmi Koyejo Program Chairs include: Alekh Agarwal Workshop Chairs include: Hanie Sedghi Tutorial Chairs include: Adji Bousso Dieng, Jessica Schrouff Affinity Workshop Chair: Adji Bousso Dieng, Jessica Schrouff Program Committee, Senior Area Chairs include: Corinna Cortes, Claudio Gentile, Mohammad Ghavamzadeh, Amir Globerson, Elad Hazan, Katherine Heller, Satyen Kale, Been Kim, Sanjiv Kumar, Hugo Larochelle, Sergey Levine, Yishay Mansour, Mehryar Mohri, Tara Sainath, Dale Schuurmans, Daniel Tarlow NeurIPS Foundation Board Secretary: Michael Mozer NeurIPS Foundation Board Members include: Corinna Cortes, Isabelle Guyon, Sanmi Koyejo, Hugo Larochelle NeurIPS Foundation Advisory Board include: Peter Bartlett, Zoubin Ghahramani, John C. Platt, Fernando Pereira, Dale Schuurmans Keynote Speakers The Data-Centric Era: How ML is Becoming an Experimental ScienceIsabelle Guyon The Forward-Forward Algorithm for Training Deep Neural NetworksGeoffrey Hinton Outstanding Paper Award Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, Mohammad Norouzi EXPO Day Workshops Graph Neural Networks in Tensorflow: A Practical GuideWorkshop Organizers include: Bryan Perozzi, Sami Abu-el-Haija A Hands-On Introduction to Tensorflow and JaxWorkshop Organizers include: Josh Gordon Affinity Workshops LatinX in AI (LXAI)Platinum Sponsor Networking & Social Chairs include: Andres Muñoz MedinaProgram Committee includes: Johan Obando Ceron Queer in AIPanelists include: Sara Beery, Talia Ringer Women in Machine Learning (WiML)Platinum Sponsor Workshop Organizers and Mentorship Chairs include: Beliz GunelMentors include: Adam Roberts, Eleni Triantafillou, Zelda Mariet, Clara Hu, Rosanne Liu, Alekh Agarwal, Vinod Prabhakaran, Rose Yu, Katherine Heller Workshops New in MLWorkshop Organizers include: Isabelle Guyon AI for Accelerated Materials Design (AI4Mat)Workshop Organizers include: Benjamin Sanchez-Lengeling All Things Attention: Bridging Different Perspectives on AttentionInvited Speakers and Panelists include: Vidhya Navalpakkam Efficient Natural Language and Speech Processing (ENLSP-II): The Future of Pre-trained ModelsInvited Speakers include: Tara Sainath, Anna HuangInvited Panelists include: Mohammad NorouziProgram Committee includes: Wenhu Chen Federated Learning: Recent Advances and New ChallengesProgram Committee includes: Kallista Bonawitz, Zachary Charles, Wenshuo Guo, Peter Kairouz, Zhaozhuo Xu, Zheng Xu Gaussian Processes, Spatiotemporal Modeling, and Decision-Making SystemsWorkshop Organizers include: Zi WangInvited Speakers include: Jasper Snoek, Carolina OsorioAdvisory Board includes: Zoubin Ghahramani Has it Trained Yet? A Workshop for Algorithmic Efficiency in Practical Neural Network TrainingWorkshop Organizers include: Zachary Nado, George Dahl, Naman Agarwal, Aakanksha ChowdheryInvited Speakers include: Aakanksha Chowdhery, Priya Goyal Human in the Loop Learning (HiLL)Workshop Organizers include: Fisher Yu, Vittorio FerrariInvited Speakers include: Dorsa Singh, Igor Mordatch, Ding Zhao INTERPOLATE — First Workshop on Interpolation Regularizers and BeyondWorkshop Organizers include: Yann DauphinInvited Speakers include: Chelsea FinnPanelists include: Chelsea Finn, Dustin TranProgram Committee includes: Wang Chen, Kimin Lee LaReL: Language and Reinforcement LearningInvited Speakers include: Dorsa Singh, Igor Mordatch Medical Imaging Meets NeurIPSProgram Committee includes: Chenyu You Memory in Artificial and Real Intelligence (MemARI)Program Committee includes: Benjamin Eysenbach, Otilia Stretcu Meta-LearningWorkshop Organizers include: Eleni TriantafillouInvited Speakers include: Lucas Byer, Chelsea FinnProgram Committee includes: Ishita Dasgupta, Praneet Dutta, Benjamin Eysenbach, Maximilian Igl, Louis Kirsch, Parsa Mahmoudieh, Marc Pickett, Eleni Triantafillou New Frontiers in Graph Learning (GLFrontiers)Workshop Organizers include: Hanjun Dai Offline Reinforcement Learning Workshop: Offline RL as a “Launchpad”Workshop Organizers include: Rishabh Agarwal, Aviral Kumar, George TuckerInvited Speakers include: Dorsa Sadigh Score-Based MethodsInvited Speakers include: Mohammad NorouziInvited Panelists include: Jascha Sohl-Dickstein Synthetic Data for Empowering ML ResearchInvited Speakers include: Mehryar MohriInvited Panelists include: Katrina LigettProgram Committee includes: Jinsung Yoon Table Representation LearningWorkshop Organizers include: Pengcheng YinInvited Speakers include: Xinyun Chen, Carsten BinnigPanelists include: Julian EisenschlosProgram Committee includes: Wenhu Chen, Xinyun Chen, Beliz Gunel A Causal View on Dynamical SystemsProgram Committee includes: Rose Yu Algorithmic Fairness Through the Lens of Causality and PrivacyWorkshop Organizers include: Awa DiengInvited Speakers include: Nicolas PapernotRoundtable Leads include: David Madras, Negar Rostamzadeh, Nyalleng MoroosiProgram Committee includes: Matt Kusner Broadening Research Collaborations in MLWorkshop Organizers include: Rosanne Liu, Pablo Samuel Castro, Sunipa Dev Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and ApplicationsInvited Speakers include: Peter Kairouz Distribution Shifts (DistShift): Connecting Methods and ApplicationsWorkshop Organizers include: Becca Roelofs, Chelsea Finn, Jacob Eisenstein, Pang Wei KohInvited Speakers include: Sarah Beery Foundation Models for Decision MakingWorkshop Organizers include: Sherry Yang, Yilun Du, Igor Mordatch, Shixiang Shane Gu,Ofir NachumInvited Speakers include: Dorsa Sadigh, Dale Schuurmans, Machel ReidProgram Committee includes: Bo Dai, Aleksandra Faust, Hiroki Furuta, Kati Goshvadi, Izzeddin Gur, Austin Huang, Kimin Lee, Kuang-Huei Lee, Lisa Lee, Yingjie Miao, Jordi Orbay, Ted Xiao Gaze Meets MLProgram Committee includes: Peter Mattson, Mehdi Moradi I Can’t Believe It’s Not Better: Understanding Deep Learning Through Empirical FalsificationWorkshop Organizers include: Javier AntoránPanelists include: Kevin Murphy Interactive Learning for Natural Language ProcessingInvited Speakers include: Anca DraganProgram Committees include: Julia Kreutzer, Shunyu Yao Machine Learning and the Physical SciencesWorkshop Organizers include: Adji Bousso DiengInvited Speakers include: Ekin Doğuş Çubuk Machine Learning for SystemsWorkshop Organizers include: Martin Maas, Azade Nova, Dan ZhangInvited Speakers include: Jeff DeanProgram Committee includes: Milad Hashemi, Kevin Swersky Machine Learning in Structural BiologyInvited Speakers include: David Fleet MATH-AI: Toward Human-Level Mathematical ReasoningWorkshop Organizers include: Swaroop Mishra, Yuhuai WuInvited Speakers include: Talia Ringer OPT 2022: Optimization for Machine LearningWorkshop Organizers include: Courtney Paquette Reinforcement Learning for Real Life (RL4RealLife)Workshop Organizers include: Minmin ChenInvited Panelists include: Pablo Samuel CastroProgram Committee includes: Victor Carbune, Bo Chang, Yinlam Chow, Konstantina Christakopoulou, Bo Dai, Hanjun Dai, Aleksandra Faust, Joshua Greaves, Chih-wei Hsu, Rahul Kidambi, Srivatsan Krishnan, Iou-Jen Liu, Cong Lu, Jincheng Mei, Chao Qin Self-Supervised Learning – Theory and PracticeInvited Speakers include: Mathilde Caron Symmetry and Geometry in Neural Representations (NeurReps)Invited Speakers include: Noah ShuttyProgram Committee includes: Ondrej Biza, Noah Shutty Temporal Graph Learning WorkshopInvited Speakers include: Mehran Kazemi Transfer Learning for Natural Language ProcessingWorkshop Organizers include: Deepak Ramachandran, Sebastian RuderInvited Speakers include: Jonas PfeifferInvited Debaters include: Ellie PavlickProgram Committee includes: Patrick Fernandes, Jonas Pfeiffer, Jiao Sun, Tu Vu, Xinyi Wang, Xin Xu Cultures of AI and AI for CultureWorkshop Organizers include: Rida Qadri, Fernando Diaz Deep Reinforcement Learning WorkshopWorkshop Organizers include: Karol Hausman, Ted Xiao, Zeyu ZhengInvited Speakers include: Igor MordatchAdvisory Board includes: Chelsea Finn Empowering Communities: A Participatory Approach to AI for Mental HealthProgram Committee includes: Diana Mincu, Subhrajit Roy, Martin Seneviratne HCAI@NeurIPS 2022, Human Centered AIKeynote Speaker includes: Fernanda Viegas Learning Meaningful Representations of LifeWorkshop Organizers include: Adji Bousso Dieng Machine Learning for Creativity and DesignWorkshop Organizers include: Yingtao Tian Machine Learning SafetyWorkshop Organizers include: Nicholas CarliniInvited Speakers include: Dorsa Sadigh Neuro Causal and Symbolic AI (nCSI)Workshop Organizers include: Thomas Kipf Robot Learning Workshop: Trustworthy RoboticsWorkshop Organizers include: Alex Bewley, Jonathan TompsonInvited Speakers include: Karol Hausman, Brian Ichter, Been Kim, Leila Takayama, Andy ZengProgram Committee includes: Vincent Vanhoucke The Symbiosis of Deep Learning and Differential Equations IIWorkshop Organizers include: Winnie XuInvited Speakers include: Rose Yu Tackling Climate Change with Machine LearningWorkshop Organizers include: Emma Strubell Trustworthy and Socially Responsible Machine LearningInvited Speakers include: Been Kim, Dorsa Sadigh, Milind Tambe Vision Transformers: Theory and ApplicationsInvited Speakers include: Cordelia Schmid, Ming Hsuan Yang Tutorials Advances in Bayesian OptimizationTutorial Organizers include: Virginia Aglietti Creative Culture and Machine LearningTutorial Organizers include: Negar Rostamzadeh Fair and Socially Responsible ML for Recommendations: Challenges and PerspectivesInvited Panelists include: Fernando Diaz Lifelong Learning MachinesInvited Panelists include: Christopher Summerfield The Role of Meta-learning for Few-Shot LearningTutorial Organizers include: Eleni TriantafillouInvited Panelists include: Neil Houlsby, Priyanka Agrawal Competitions NeurIPS 2022 Competition Track: Overview & ResultsInvited Speakers include: Isabelle Guyon Causal Insights for Learning Paths in EducationCompetition Organizers include: Zichao (Jack) Wang IGLU: Interactive Grounded Language Understanding in a Collaborative EnvironmentCompetition Organizers include: Negar Arabzadeh Cross-Domain MetaDL: Any-Way Any-Shot Learning Competition with Novel Datasets from Practical DomainsCompetition Organizers include: Isabelle Guyon Reconnaissance Blind Chess: An Unsolved Challenge for Multi-Agent Decision Making Under UncertaintyCompetition Organizers include: Bo Li VisDA 2022 Challenge: Sim2Real Domain Adaptation for Industrial RecyclingCompetition Organizers include: Dina Bashkirova Spotlight Papers CoPur: Certifiably Robust Collaborative Inference via Feature PurificationJing Liu, Chulin Xie, Oluwasanmi O Koyejo, Bo Li Machine Learning on Graphs: A Model and Comprehensive TaxonomyInes Chami*, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy Sparse Winning Tickets are Data-Efficient Image RecognizersMukund Varma T, Xuxi Chen, Zhenyu Zhang, Tianlong Chen, Subhashini Venugopalan, Zhangyang Wang Federated Learning from Pre-trained Models: A Contrastive Learning ApproachYue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, Jing Jiang Improving Multi-task Generalization via Regularizing Spurious CorrelationZiniu Hu*, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, Ed H. Chi The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement LearningYunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare Residual Multiplicative Filter Networks for Multiscale ReconstructionShayan Shekarforoush, David B. Lindell, David J. Fleet, Marcus A Brubaker Differentially Private Learning with Margin GuaranteesRaef Bassily, Mehryar Mohri, Ananda Theertha Suresh Optimal Query Complexities for Dynamic Trace EstimationDavid P. Woodruff*, Fred Zhang*, Qiuyi Zhang Papers From Gradient Flow on Population Loss to Learning with Stochastic Gradient DescentAyush Sekhari, Satyen Kale, Jason D. Lee, Chris De Sa, Karthik Sridharan On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential GamesRunyu Zhang, Jincheng Mei, Bo Dai, Dale Schuurmans, Na Li Matryoshka Representation LearningAditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham Kakade, Prateek Jain, Ali Farhadi Efficient Risk-Averse Reinforcement LearningIdo Greenberg, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor Operator Splitting Value IterationAmin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-massoud Farahmand Cluster Randomized Designs for One-Sided Bipartite ExperimentsJennifer Brennan*, Vahab Mirrokni, Jean Pouget-Abadie A Unified Sequence Interface for Vision TasksTing Chen, Saurabh Saxena, Lala Li, Tsung-Yi Lin*, David J. Fleet, Geoffrey Hinton Cryptographic Hardness of Learning Halfspaces with Massart NoiseIlias Diakonikolas, Daniel M. Kane, Pasin Manurangsi, Lisheng Ren Better Best of Both Worlds Bounds for Bandits with Switching CostsIdan Amir, Guy Azov, Tomer Koren, Roi Livni Fast Neural Kernel Embeddings for General ActivationsInsu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic DepthLaxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirronki, Jessica Shi Improving Zero-Shot Generalization in Offline Reinforcement Learning Using Generalized Similarity FunctionsBogdan Mazoure*, Ilya Kostrikov, Ofir Nachum, Jonathan Tompson Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial ExamplesMaura Pintor, Luca Demetrio, Angelo Sotgiu, Ambra Demontis, Nicholas Carlini, Battista Biggio, Fabio Roli Learning Energy Networks with Generalized Fenchel-Young LossesMathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist Learning Robust Dynamics Through Variational Sparse GatingArnav Kumar Jain, Shiva Kanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, Samira Ebrahimi Kahou Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean MeasuresArnav Kumar Jain, Shiva Kanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, Samira Ebrahimi Kahou So3krates: Equivariant Attention for Interactions on Arbitrary Length-Scales in Molecular SystemsJ. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller Spectral Bias in Practice: The Role of Function Frequency in GeneralizationSara Fridovich-Keil*, Raphael Gontijo-Lopes, Rebecca Roelofs Delving into Out-of-Distribution Detection with Vision-Language RepresentationsYifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun, Wei Li, Yixuan Li Path Independent Equilibrium Models Can Better Exploit Test-Time ComputationCem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein, Yuhuai Wu, Shaojie Bai, J. Zico Kolter, Roger Grosse On Optimal Learning Under Targeted Data PoisoningSteve Hanneke, Amin Karbasi, Mohammad Mahmoody, Idan Mehalel, Shay Moran Learning With Little MixingIngvar Ziemann, Stephen Tu Block-Recurrent TransformersDeLesley Hutchins, Imanol Schlag*, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur TabNAS: Rejection Sampling for Neural Architecture Search on Tabular DatasetsChengrun Yang, Gabriel Bender, Hanxiao Liu, Pieter-Jan Kindermans, Madeleine Udell, Yifeng Lu, Quoc Le, Da Huang Regret Bounds for Multilabel Classification in Sparse Label RegimesRobert Busa-Fekete, Heejin Choi, Krzysztof Dembczynski, Claudio Gentile, Henry William Reeve, Balazs Szorenyi Robust Reinforcement Learning Using Offline DataKishan Panaganti, Zaiyan Xu, Dileep Kalathil, Mohammad Ghavamzadeh Contrastive Learning as Goal-Conditioned Reinforcement LearningBenjamin Eysenbach, Tianjun Zhang, Sergey Levine, Ruslan Salakhutdinov Beyond Rewards: A Hierarchical Perspective on Offline Multiagent Behavioral AnalysisShayegan Omidshafiei, Andrei Kapishnikov, Yannick Assogba, Lucas Dixon, Been Kim Revisiting Neural Scaling Laws in Language and VisionIbrahim Alabdulmohsin, Behnam Neyshabur, Xiaohua Zhai Polynomial Neural Fields for Subband Decomposition and ManipulationGuandao Yang*, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan T. Barron, Thomas Funkhouser, Bharath Hariharan, Serge Belongie First Is Better Than Last for Language Data InfluenceChih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar The Privacy Onion Effect: Memorization Is RelativeNicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramer Deep Hierarchical Planning from Pixels (see blog post) Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel Discovered Policy OptimisationChris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schroeder de Witt, Jakob Foerster Semi-supervised Active Linear RegressionFnu Devvrit, Nived Rajaraman, Pranjal Awasthi Pruning’s Effect on Generalization Through the Lens of Training and RegularizationTian Jin, Daniel M. Roy, Michael Carbin, Jonathan Frankle, Gintare Karolina Dziugaite Exploring Length Generalization in Large Language ModelsCem Anil*, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm Under ParallelizationBenjamin Dubois-Taine, Francis Bach, Quentin Berthet, Adrien Taylor Global Normalization for Streaming Speech Recognition in a Modular FrameworkEhsan Variani, Ke Wu, Michael Riley, David Rybach, Matt Shannon, Cyril Allauzen Learning Predictions for Algorithms with PredictionsMikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts (see blog post) Basil Mustafa, Carlos Riquelme, Joan Puigcerver, Rodolphe Jenatton, Neil Houlsby Incrementality Bidding via Reinforcement Learning Under Mixed and Delayed RewardsAshwinkumar Badanidiyuru, Zhe Feng, Tianxi Li, Haifeng Xu* Solving Quantitative Reasoning Problems with Language Models (see blog post) Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra Anonymized Histograms in Intermediate Privacy ModelsBadih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi Efficient and Stable Fully Dynamic Facility LocationSayan Bhattacharya, Nikos Parotsidis, Silvio Lattanzi Are All Losses Created Equal: A Neural Collapse PerspectiveJinxin Zhou, Chong You, Xiao Li, Kangning Liu, Sheng Liu, Qing Qu, Zhihui Zhu Universal Rates for Interactive LearningSteve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial CorruptionsJiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept ClassesAlkis Kalavasis, Grigoris Velegkas, Amin Karbasi Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence LearningCenk Baykal, Nishanth Dikkala, Rina Panigrahy, Cyrus Rashtchian, Xin Wang Pre-trained Language Models for Interactive Decision-Making Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, Jacob Andreas, Igor Mordatch, Antonio Torralba, Yuke Zhu Polynomial Neural Fields for Subband Decomposition and Manipulation Guandao Yang*, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan T. Barron, Thomas Funkhouser, Bharath Hariharan, Serge Belongie Submodular Maximization in Clean Linear Time Wenxin Li, Moran Feldman, Ehsan Kazemi, Amin Karbasi Reinforcement Learning with Logarithmic Regret and Policy Switches Grigoris Velegkas, Zhuoran Yang, Amin Karbasi Algorithms with Prediction Portfolios Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii Understanding and Improving Robustness of Vision Transformers Through Patch-Based Negative Augmentation Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang Best of Both Worlds Model Selection Aldo Pacchiano, Christoph Dann, Claudio Gentile Fair Wrapping for Black-Box Predictions Alexander Soen, Ibrahim Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie A Reduction to Binary Approach for Debiasing Multiclass Datasets Ibrahim Alabdulmohsin, Jessica Schrouff, Oluwasanmi Koyejo Weighted Distillation with Unlabeled Examples Fotis Iliopoulos, Vasilis Kontonis, Cenk Baykal, Gaurav Menghani, Khoa Trihn,Erik Vee A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases James Harrison, Luke Metz, Jascha Sohl-Dickstein Post-hoc Estimators for Learning to Defer to an Expert Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar Model-Based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity Alekh Agarwal, Tong Zhang On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal Towards Learning Universal Hyperparameter Optimizers with Transformers (see blog post) Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Qiuyi Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc’aurelio Ranzato, Sagi Perel, Nando de Freitas Reproducibility in Optimization: Theoretical Framework and Limits Kwangjun Ahn*, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir Confident Adaptive Language Modeling Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Q. Tran, Yi Tay, Donald Metzler Reinforcement Learning with Neural Radiance Fields Danny Driess, Ingmar Schubert, Pete Florence, Yunzhu Li, Marc Toussaint Invariant and Transportable Representations for Anti-Causal Domain Shifts Yibo Jiang, Victor Veitch Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions Gagan Aggarwal, Kshipra Bhawalkar, Aranyak Mehta, Divyarthi Mohan, Alexandros Psomas STaR: Bootstrapping Reasoning with Reasoning Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah D. Goodman Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality Teodor V. Marinov, Mehryar Mohri, Julian Zimmert The Curse of Unrolling: Rate of Differentiating Through Optimization Damien Scieur, Quentin Bertrand, Gauthier Gidel, Fabian Pedregosa Visual Prompting via Image Inpainting Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, Alexei A Efros Multi-Class H-Consistency Bounds Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong Anonymous Bandits for Multi-User Systems Hossein Esfandiari, Vahab Mirrokni, Jon Schneider Understanding the Eluder Dimension Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro Why So Pessimistic? Estimating Uncertainties for Offline RL Through Ensembles, and Why Their Independence Matters Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback Saeed Masoudian, Julian Zimmert, Yevgeny Seldin A Theoretical View on Sparsely Activated Networks Cenk Baykal, Nishanth Dikkala, Rina Panigrahy, Cyrus Rashtchian, Xin Wang Chain of Thought Prompting Elicits Reasoning in Large Language Models (see blog post) Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou Decoupled Context Processing for Context Augmented Language Modeling Zonglin Li, Ruiqi Guo, Sanjiv Kumar Exploring Through Random Curiosity with General Value Functions Aditya Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber Object Scene Representation Transformer Mehdi S. M. Sajjadi, Daniel Duckworth, Aravindh Mahendran, Sjoerd van Steenkiste, Filip Pavetić, Mario Lučić, Leonidas J. Guibas, Klaus Greff, Thomas Kipf Joint Model-Policy Optimization of a Lower Bound for Model-Based RL Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov A Fourier Approach to Mixture Learning Mingda Qiao*, Guru Guruganesh, Ankit Singh Rawat, Avinava Dubey, Manzil Zaheer Why Neural Networks Find Simple Solutions: The Many Regularizers of Geometric Complexity Benoit Dherin, Michael Munn, Mihaela Rosca, David Barrett Do Current Multi-task Optimization Methods in Deep Learning Even Help? Derrick Xin, Behrooz Ghorbani, Ankush Garg, Orhan Firat, Justin Gilmer Associating Objects and Their Effects in Video Through Coordination Games Erika Lu, Forrester Cole, Weidi Xie, Tali Dekel, William Freeman, Andrew Zisserman, Michael Rubinstein Increasing Confidence in Adversarial Robustness Evaluations Roland S. Zimmermann*, Wieland Brendel, Florian Tramèr, Nicholas Carlini The Role of Baselines in Policy Gradient Optimization Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvari, Dale Schuurmans Scaling Multimodal Pre-training via Cross-Modality Gradient Harmonization Junru Wu, Yi Liang, Feng Han, Hassan Akbari, Zhangyang Wang, Cong Yu* S3GC: Scalable Self-Supervised Graph Clustering Fnu Devvrit*, Aditya Sinha, Inderjit Dhillon, Prateek Jain Algorithms and Hardness for Learning Linear Thresholds from Label Proportions Rishi Saket ALMA: Hierarchical Learning for Composite Multi-Agent Tasks Shariq Iqbal, Robby Costales, Fei Sha DC-BENCH: Dataset Condensation Benchmark Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh Does GNN Pre-training Help Molecular Representation? Ruoxi Sun, Hanjun Dai, Adams Yu Drawing Out of Distribution with Neuro-Symbolic Generative Models Yichao Liang, Joshua B. Tenenbaum, Tuan Anh Le, N. Siddharth Mixture-of-Experts with Expert Choice Routing (see blog post) Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew Dai, Zhifeng Chen, Quoc Le, James Laudon Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback Tiancheng Jin, Tal Lancewicki, Haipeng Luo, Yishay Mansour, Aviv Rosenberg Precise Learning Curves and Higher-Order Scalings for Dot-Product Kernel Regression Lechao Xiao, Jeffrey Pennington, Theodor Misiakiewicz, Hong Hu, Yue Lu Rate-Optimal Online Convex Optimization in Adaptive Linear Control Asaf Cassel, Alon Cohen, Tomer Koren Why Neural Networks Find Simple Solutions: The Many Regularizers of Geometric Complexity Benoit Dherin, Michael Munn, Mihaela Rosca, David G.T. Barrett Private Isotonic Regression Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi Sketching Based Representations for Robust Image Classification with Provable Guarantees Nishanth Dikkala, Sankeerth Rao Karingula, Raghu Meka, Jelani Nelson, Rina Panigrahy, Xin Wang The Role of Baselines in Policy Gradient Optimization Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvari, Dale Schuurmans Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens Elad Ben Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson Near-Optimal Private and Scalable k-Clustering Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan*, Peilin Zhong When Does Differentially Private Learning Not Suffer in High Dimensions? Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A Inan, Janardhan Kulkarni, YinTat Lee, Abhradeep Guha Thakurta End-to-End Learning to Index and Search in Large Output Spaces Nilesh Gupta, Patrick H. Chen, Hsiang-Fu, Yu, Cho-Jui Hsieh, Inderjit S. Dhillon A Boosting Approach to Reinforcement Learning Nataly Brukhim, Elad Hazan, Karan Singh FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction Samiul Alam, Luyang Liu, Ming Yan, Mi Zhang Non-Convex Online Learning via Algorithmic Equivalence Udaya Ghai, Zhou Lu, Elad Hazan Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Karthikeyan Shanmugam SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos Gamaleldin F. Elsayed, Aravindh Mahendran, Sjoerd van Steenkiste, Klaus Greff, Michael C. Mozer, Thomas Kipf UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes Alexander Kolesnikov, André Susano Pinto, Lucas Beyer, Xiaohua Zhai, Jeremiah Harmsen, Neil Houlsby Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington Multi-game Decision Transformers (see blog post) Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski, Igor Mordatch Subsidiary Prototype Alignment for Universal Domain Adaptation Jogendra Nath Kundu, Suvaansh Bhambri, Akshay Ravindra Kulkarni, Hiran Sarkar, Varun Jampani, Venkatesh Babu Radhakrishnan SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections Mark Boss*, Andreas Engelhardt*, Abhishek Kar, Yuanzhen Li, Deqing Sun, Jonathan T. Barron, Hendrik P. A. Lensch, Varun Jampani Chefs’ Random Tables: Non-Trigonometric Random Features Valerii Likhosherstov, Krzysztof Marcin Choromanski, Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks Mansheej Paul, Brett W Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite DP-PCA: Statistically Optimal and Differentially Private PCA Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh Emergent Communication: Generalization and Overfitting in Lewis Games Mathieu Rita, Corentin Tallec, Paul Michel, Jean-Bastien Grill, Olivier Pietquin, Emmanuel Dupoux, Florian Strub Handcrafted Backdoors in Deep Neural Networks Sanghyun Hong, Nicholas Carlini, Alexey Kurakin I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification Muhammad Ferjad Naeem, Yongqin Xian, Luc Van Gool, Federico Tombari Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams Sergey Denisov, Brendan McMahan, Keith Rush, Adam Smith, Abhradeep Guha Thakurta Optimal Scaling for Locally Balanced Proposals in Discrete Spaces Haoran Sun*, Hanjun Dai, Dale Schuurmans Near-Optimal Correlation Clustering with Privacy Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers Albert Q. Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygóźdź, Piotr Miłoś, Yuhuai Wu, Mateja Jamnik TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer, Sanjiv Kumar When Does Dough Become a Bagel? Analyzing the Remaining Mistakes on ImageNet Vijay Vasudevan, Benjamin Caine, Raphael Gontijo-Lopes, Sara Fridovich-Keil, Rebecca Roelofs DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar A Characterization of Semi-Supervised Adversarially Robust PAC Learnability Idan Attias, Steve Hanneke, Yishay Mansour Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation Ziyu Jiang, Xuxi Chen, Xueqin Huang, Xianzhi Du, Denny Zhou, Zhangyang Wang Subquadratic Kronecker Regression with Applications to Tensor Decomposition Matthew Fahrbach, Gang Fu, Mehrdad Ghadiri Zero-Shot Transfer Learning Within a Heterogeneous Graph via Knowledge Transfer Networks Minji Yoon*, John Palowitch, Dustin Zelle, Ziniu Hu*, Ruslan Salakhutdinov, Bryan Perozzi Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress (see blog post) Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, Marc G. Bellemare Private and Communication-Efficient Algorithms for Entropy Estimation Gecia Bravo-Hermsdorff, Robert Busa-Fekete, Mohammad Ghavamzadeh, Andres Munoz Medina, Umar Syed Oracle Inequalities for Model Selection in Offline Reinforcement Learning Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill Diagnosing Failures of Fairness Transfer Across Distribution Shift in Real-World Medical Settings Jessica Schrouff*, Natalie Harris, Oluwasanmi O 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 A Heller, Silvia Chiappa, Alexander D’Amour 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 Patching Open-Vocabulary Models by Interpolating Weights Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, Ludwig Schmidt TUSK: Task-Agnostic Unsupervised Keypoints Yuhe Jin, Weiwei Sun, Jan Hosang, Eduard Trulls, Kwang Moo Yi Active Learning of Classifiers with Label and Seed Queries Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice, Maximilian Thiessen Autoformalization with Large Language Models Yuhuai Wu, Albert Q. Jiang, Wenda Li, Markus N. Rabe, Charles Staats, Mateja Jamnik, Christian Szegedy Benign Underfitting of Stochastic Gradient Descent Tomer Koren, Roi Livni, Yishay Mansour, Uri Sherman Chain of Thought Imitation with Procedure Cloning Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum Efficient and Modular Implicit Differentiation Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert Insights into Pre-training via Simpler Synthetic Tasks Yuhuai Wu, Felix Li, Percy Liang Self-Supervised Learning with an Information Maximization Criterion Serdar Ozsoy, Shadi Hamdan, Sercan Ö. Arik, Deniz Yuret, Alper T. Erdogan Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model Weihao Kong, Rajat Sen, Pranjal Awasthi, Abhimanyu Das Using Embeddings for Causal Estimation of Peer Influence in Social Networks Irina Cristali, Victor Veitch VCT: A Video Compression Transformer Fabian Mentzer, George Toderici, David Minnen, Sung-Jin Hwang, Sergi Caelles, Mario Lucic, Eirikur Agustsson Video Diffusion Models Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, David J. Fleet Large Language Models are Zero-Shot Reasoners Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa Improved Coresets for Euclidean k-Means Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar On the Adversarial Robustness of Mixture of Experts Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme Ruiz, Pranjal Awasthi, Srinadh Bhojanapalli Stars: Tera-Scale Graph Building for Clustering and Learning CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement Erik Wijmans, Irfan Essa, Dhruv Batra TaSIL: Taylor Series Imitation Learning Daniel Pfrommer, Thomas TCK Zhang, Stephen Tu, Nikolai Matni RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks Leo Kozachkov, Michaela M Ennis, Jean-Jacques Slotine Integral Probability Metrics PAC-Bayes Bounds Ron Amit, Baruch Epstein, Shay Moran, Ron Meir D2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video Tianhao Wu, Fangcheng Zhong, Andrea Tagliasacchi, Forrester Cole, Cengiz Oztireli Posted Pricing and Dynamic Prior-Independent Mechanisms with Value Maximizers Yuan Deng, Vahab Mirrokni, Hanrui Zhang Transformer Memory as a Differentiable Search Index Yi Tay, Vinh Q. Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, Tal Schuster, William W. Cohen, Donald Metzler *Work done while at Google. ↩