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**[email protected] 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***, Yizhou Sun,*

**Tiansheng Yao, Lichan Hong**

**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,*

*, Sham Kakade,*

**Kaifeng Chen***, Ali Farhadi*

**Prateek Jain**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***, Tsung-Yi Lin*

**Lala Li**^{*},

*,*

**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***, Jessica Shi*

**Vahab Mirronki**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***, Madeleine Udell,*

**Pieter-Jan Kindermans***,*

**Yifeng Lu***,*

**Quoc Le**

**Da Huang**Regret Bounds for Multilabel Classification in Sparse Label Regimes* Robert Busa-Fekete*,

*, Krzysztof Dembczynski,*

**Heejin Choi***, Henry William Reeve, Balazs Szorenyi*

**Claudio Gentile**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,

*, Ruslan Salakhutdinov*

**Sergey Levine**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***, Bharath Hariharan, Serge Belongie*

**Thomas Funkhouser**First Is Better Than Last for Language Data Influence* Chih-Kuan Yeh*,

*,*

**Ankur Taly***,*

**Mukund Sundararajan***, Pradeep Ravikumar*

**Frederick Liu**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***, Pieter Abbeel*

**Ian Fischer**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*,

*, Tianxi Li, Haifeng Xu*

**Zhe Feng**^{*}

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*,

*, Grigoris Velegkas*

**Shay Moran**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***, Bharath Hariharan, Serge Belongie*

**Thomas Funkhouser** 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***, Ke Sun, Lexing Xie*

**Richard Nock** 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***, Kazuya Kawakami,*

**David Dohan***, Arnaud Doucet, Marc'aurelio Ranzato,*

**Greg Kochanski***,*

**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,

*, Yutao Zhong*

**Mehryar Mohri** 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,

*, Ruslan Salakhutdinov*

**Sergey Levine** A Fourier Approach to Mixture Learning

Mingda Qiao^{*}, * Guru Guruganesh*,

*,*

**Ankit Singh Rawat***, Manzil Zaheer*

**Avinava Dubey** 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***, Zhangyang Wang, Cong Yu*

**Hassan Akbari**^{*}

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*,

*, Mihaela Rosca, David G.T. Barrett*

**Michael Munn** Private Isotonic Regression

* Badih Ghazi*,

*,*

**Pritish Kamath***,*

**Ravi Kumar**

**Pasin Manurangsi** Sketching Based Representations for Robust Image Classification with Provable Guarantees

* Nishanth Dikkala*,

*, Raghu Meka, Jelani Nelson,*

**Sankeerth Rao Karingula***,*

**Rina Panigrahy**

**Xin Wang** The Role of Baselines in Policy Gradient Optimization

* Jincheng Mei*, Wesley Chung, Valentin Thomas,

*, Csaba Szepesvari,*

**Bo Dai**

**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***, Hendrik P. A. Lensch,*

**Jonathan T. Barron**

**Varun Jampani** Chefs’ Random Tables: Non-Trigonometric Random Features

Valerii Likhosherstov, * Krzysztof Marcin Choromanski*,

*,*

**Avinava Dubey***,*

**Frederick Liu***, Adrian Weller*

**Tamas Sarlos** 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*,

*, Adam Smith,*

**Keith Rush**

**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,

*, Slobodan Mitrović,*

**Silvio Lattanzi***,*

**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*,

*, Zhangyang Wang*

**Denny Zhou** Subquadratic Kronecker Regression with Applications to Tensor Decomposition

* Matthew Fahrbach*,

*, Mehrdad Ghadiri*

**Gang Fu** Zero-Shot Transfer Learning Within a Heterogeneous Graph via Knowledge Transfer Networks

Minji Yoon^{*}, * John Palowitch*,

*, Ziniu Hu*

**Dustin Zelle**^{*}, 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***, Aaron Courville,*

**Pablo Samuel Castro**

**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***, Emma Brunskill*

**Bo Dai** Diagnosing Failures of Fairness Transfer Across Distribution Shift in Real-World Medical Settings

Jessica Schrouff^{*},* Natalie Harris*,

*,*

**Oluwasanmi O Koyejo***, Eva Schnider*

**Ibrahim Alabdulmohsin**^{*},

*,*

**Krista Opsahl-Ong***,*

**Alexander Brown***,*

**Subhrajit Roy***,*

**Diana Mincu***,*

**Christina Chen***,*

**Awa Dieng***,*

**Yuan Liu***,*

**Vivek Natarajan***,*

**Alan Karthikesalingam***, Silvia Chiappa,*

**Katherine A Heller**

**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*,

*, Kwang Moo Yi*

**Eduard Trulls** 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***, Mateja Jamnik,*

**Charles Staats**

**Christian Szegedy** Benign Underfitting of Stochastic Gradient Descent

* Tomer Koren*, Roi Livni,

*, Uri Sherman*

**Yishay Mansour** Chain of Thought Imitation with Procedure Cloning

* Mengjiao Yang*,

*, Pieter Abbeel,*

**Dale Schuurmans**

**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.

^{↩}