Google at ICML 2025

Avoiding Spurious Sharpness Minimization Broadens Applicability of SAM
Sidak Pal Singh, Hossein Mobahi, Atish Agarwala, Yann Nicolas Dauphin

Can RLHF Be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective
Jiawei Huang, Bingcong Li, Christoph Dann, Niao He

Competing Bandits in Matching Markets via Super Stability
Soumya Basu

Does Learning the Right Latent Variables Necessarily Improve In-Context Learning?
Sarthak Mittal, Eric Elmoznino, Léo Gagnon, Sangnie Bhardwaj, Guillaume Lajoie, Dhanya Sridhar

GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance
Jinuk Kim*, Marwa El Halabi, Wonpyo Park, Clemens JS Schaefer, Deokjae Lee, Yeonhong Park, Jae W. Lee, Hyun Oh Song

The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steering
Zhuowei Li, Haizhou Shi, Yunhe Gao, Di Liu, Zhenting Wang, Yuxiao Chen, Ting Liu, Long Zhao, Hao Wang, Dimitris N. Metaxas

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks
Walter Mayor, Johan Obando-Ceron, Aaron Courville, Pablo Samuel Castro

Improving the Variance of Differentially Private Randomized Experiments Through Clustering
Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie

It's My Data Too: Private ML for Datasets with Multi-User Training Examples
Arun Ganesh, Ryan McKenna, Brendan McMahan, Adam Smith, Fan Wu*

LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models
Fanfei Li, Thomas Klein, Wieland Brendel, Robert Geirhos, Roland S. Zimmermann

Learning-Order Autoregressive Models with Application to Molecular Graph Generation
Zhe Wang, Jiaxin Shi, Nicolas Heess, Arthur Gretton, Michalis Tistias

Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding
Tian Jin*, Ellie Cheng, Zachary Ankner, Nikunj Saunshi, Blake Elias, Amir Yazdanbakhsh, Jonathan Ragan-Kelley, Suvinay Subramanian, Michael Carbin

LLM Alignment as Retriever Optimization: An Information Retrieval Perspective
Bowen Jin, Jinsung Yoon, Zhen Qin, Ziqi Wang, Wei Xiong, Yu Meng, Jiawei Han, Sercan Ö. Arik

LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations
Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li, Vlad Mnih, Tim Genewin

Masked Generative Nested Transformers with Decode Time Scaling
Sahil Goyal, Debapriya Tula*, Gagan Jain, Pradeep Shenoy, Prateek Jain, Sujoy Paul

On the Learnability of Distribution Classes with Adaptive Adversaries
Tosca Lechner, Alex Bie, Gautam Kamath

Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures
Jie Gao, Rajesh Jayaram, Benedikt Kolbe, Shay Sapir, Chris Schwiegelshohn, Sandeep Silwal, Erik Waingarten

Relative Error Fair Clustering in the Weak-Strong Oracle Model
Vladimir Braverman, Prathamesh Dharangutte, Shaofeng Jiang, Hoai-An Nguyen, Chen Wang, Yubo Zhang, Samson Zhou

Solving Zero-Sum Convex Markov Games
Fivos Kalogiannis, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Ian Gemp, Georgios Piliouras

Synthetic Text Generation for Training Large Language Models via Gradient Matching
Dang Nguyen, Zeman Li, MohammadHossein Bateni, Vahab Mirrokni, Meisam Razaviyayn, Baharan Mirzasoleiman

A Bayesian Model Selection Criterion for Selecting Pretraining Checkpoints
Michael Munn, Susan Wei

Accelerated Diffusion Models via Speculative Sampling
Valentin De Bortoli, Alexandre Galashov, Arthur Gretton, Arnaud Doucet

Adapting to Evolving Adversaries with Regularized Continual Robust Training
Sihui Dai, Christian Cianfarani, Arjun Bhagoji, Vikash Sehwag, Prateek Mittal

An Analysis of Quantile Temporal-Difference Learning
Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney

Approximate Differential Privacy of the L2 Mechanism
Matthew Joseph, Alex Kulesza, Alexander Yu

Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data
Corinna Cortes, Anqi Mao, Mehryar Mohri, Yutao Zhong

Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression
Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu

Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data
David Heurtel-Depeiges*, Anian Ruoss, Joel Veness, Tim Genewein

Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning
Ian Gemp, Andreas Haupt, Luke Marris, Siqi Liu, Georgios Piliouras

Design Considerations in Offline Preference-Based RL
Alekh Agarwal, Christoph Dann, Teodor Marinov

Empirical Privacy Variance
Yuzheng Hu, Fan Wu, Ruicheng Xian, Yuhang Liu, Lydia Zakynthinou, Pritish Kamath, Chiyuan Zhang, David Forsyth

EvoControl: Multi-Frequency Bi-Level Control for High-Frequency Continuous Control
Samuel Holt*, Dhruva Tirumala, Todor Davchev, Ben Moran, Atil Iscen, Antoine Laurens, Yixin Lin, Erik Frey, Francesco Romano, Markus Wulfmeier, Nicolas Heess

Improving Transformer World Models for Data-Efficient RL
Antoine Dedieu, Joseph Ortiz, Xinghua Lou, Carter Wendelken, Wolfgang Lehrach, J. Swaroop Guntupalli, Miguel Lazaro-Gredilla, Kevin Murphy

Position: Machine Learning Models Have a Supply Chain Problem
Sarah Meiklejohn, Hayden Blauzvern, Mihai Maruseac, Spencer Schrock, Laurent Simon, Ilia Shumailov

Retraining with Predicted Hard Labels Provably Increases Model Accuracy
Rudrajit Das*, Inderjit S Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong

Scalable Private Partition Selection via Adaptive Weighting
Justin Y. Chen, Vincent Cohen-Addad, Alessandro Epasto, Morteza Zadimoghaddam

SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-Training
Tianzhe Chu, Yuexiang Zhai, Jihan Yang, Shengbang Tong, Saining Xie, Dale Schuurmans, Quoc V. Le, Sergey Levine, Yi Ma

Stochastic Deep Restoration Priors for Imaging Inverse Problems
Yuyang Hu, Albert Peng, Weijie Gan, Peyman Milanfar, Mauricio Delbracio, Ulugbek Kamilov

A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
Ibrahim Alabdulmohsin, Andreas Steiner

Wasserstein Policy Optimization
David Pfau, Ian Davies, Diana Borsa, João G. M. Araújo, Brendan Tracey, Hado van Hasselt

Can Transformers Reason Logically? A Study in SAT Solving
Leyan Pan, Vijay Ganesh, Jacob Abernethy, Chris Esposo, Wenke Lee

DeepCrossAttention: Supercharging Transformer Residual Connections
Mike Heddes, Adel Javanmard, Kyriakos Axiotis, Thomas Fu, MohammadHossein Bateni, Vahab Mirrokni

Diffusion Instruction Tuning
Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare

Distributional Diffusion Models with Scoring Rules
Valentin De Bortoli, Alexandre Galashov, J. Swaroop Guntupalli, Guangyao Zhou, Kevin Murphy, Arthur Gretton, Arnaud Doucet

From Jack of All Trades to Master of One: Specializing LLM-Based Autoraters to a Test Set
Mara Finkelstein, Daniel Deutsch, Parker Riley, Juraj Juraska, Geza Kovacs, Markus Freitag

Integer Programming for Generalized Causal Bootstrap Designs
Jennifer Brennan, Sébastien Lahaie, Adel Javanmard, Nick Doudchenko*, Jean Pouget-Abadie

MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities Against Hard Perturbations
Kaixuan Huang, Jiacheng Guo, Zihao Li, Xiang Ji, Jiawei Ge, Wenzhe Li, Yingqing Guo, Tianle Cai, Hui Yuan, Runzhe Wang, Yue Wu, Ming Yin, Shange Tang, Yangsibo Huang, Chi Jin, Xinyun Chen, Chiyuan Zhang, Mengdi Wang

Matryoshka Quantization
Pranav Nair, Puranjay Datta, Jeff Dean, Prateek Jain, Aditya Kusupati

Near-Optimal Regret Using Policy Optimization in Online MDPs with Aggregate Bandit Feedback
Tal Lancewicki, Yishay Mansour

Overestimation in LLM Evaluation: A Controlled Large-Scale Study on Data Contamination’s Impact on Machine Translation
Muhammed Yusuf Kocyigit*, Eleftheria Briakou, Daniel Deutsch, Jiaming Luo, Colin Cherry, Markus Freitag

Principled Algorithms for Optimizing Generalized Metrics in Binary Classification
Anqi Mao, Mehryar Mohri, Yutao Zhong

QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions
Zhun Deng, Tom Zollo, Benjamin Eyre, Amogh Inamdar, David Madras, Richard Zemel

Structured Preconditioners in Adaptive Optimization: A Unified Analysis
Shuo Xie, Tianhao Wang, Sashank Reddi, Sanjiv Kumar, Zhiyuan Li

Theoretical Guarantees on the Best-of-n Alignment Policy
Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander D'Amour, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh

Three-Dimensional Trajectory Prediction with 3DMoTraj Dataset
Hao Zhou, Xu Yang, Mingyu Fan, Lu Qi, Ming-Hsuan Yang, Fei Luo

What Makes a Good Feedforward Computational Graph?
Alex Vitvitskyi, João G. M. Araújo, Marc Lackenby, Petar Veličković

Almost Optimal Fully Dynamic k-Center Clustering with Recourse
Sayan Bhattacharya, Martín Costa, Ermiya Farokhnejad, Silvio Lattanzi, Nikos Parotsidis

AuPair: Golden Example Pairs for Code Repair
Aditi Mavalankar, Hassan Mansoor, Zita Marinho, Masha Samsikova, Tom Schaul

Best of Both Worlds: Advantages of Hybrid Graph Sequence Models
Ali Behrouz, Ali Parviz, Mahdi Karami, Clayton Sanford, Bryan Perozzi, Vahab Mirrokni

Bipartite Ranking from Multiple Labels: On Loss Versus Label Aggregation
Michal Lukasik, Lin Chen, Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Felix X. Yu, Sashank J. Reddi, Gang Fu, Mohammadhossein Bateni, Sanjiv Kumar

Fast Tensor Completion via Approximate Richardson Iteration
Mehrdad Ghadiri, Matthew Fahrbach, Yunbum Kook, Ali Jadbabaie

Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts
Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alan Aspuru-Guzik, Arnaud Doucet, Rob Brekelmans, Alexander Tong, Kirill Neklyudov

FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
Virginia Aglietti, Ira Ktena, Jessica Schrouff*, Eleni Sgouritsa, Francisco J. R. Ruiz, Alan Malek, Alexis Bellot, Silvia Chiappa

The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence
Tom Wollschläger, Jannes Elstner, Simon Markus Geisler, Vincent Cohen-Addad, Stephan Günnemann, Johannes Gasteiger

Hardware and Software Platform Inference
Cheng Zhang, Hanna Foerster, Robert D. Mullins, Yiren Zhao, Ilia Shumailov

How Expressive Are Knowledge Graph Foundation Models?
Xingyue Huang, Pablo Barceló, Michael Bronstein, İsmail Ceylan, Mikhail Galkin, Juan Reutter, Miguel Romero Orth

In-Context Fine-Tuning for Time-Series Foundation Models
Matthew Faw*, Rajat Sen, Yichen Zhou, Abhimanyu Das

Learning From Others' Mistakes: Finetuning Machine Translation Models with Span-level Error Annotations
Lily H. Zhang, Hamid Dadkhahi, Mara Finkelstein, Firas Trabelsi, Jiaming Luo, Markus Freitag

The Limits of Predicting Agents from Behaviour
Alexis Bellot, Jonathan Richens, Tom Everitt

LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models
Marwa Abdulhai, Isadora White, Charlie Snell, Charles Sun, Joey Hong, Yuexiang Zhai, Kelvin Xu, Sergey Levine

Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defer
Anqi Mao, Mehryar Mohri, Yutao Zhong

On Mitigating Affinity Bias Through Bandits with Evolving Biased Feedback
Matthew Faw, Constantine Caramanis, Jessica Hoffmann

Position: Iterative Online-Offline Joint Optimization Is Needed to Manage Complex LLM Copyright Risks
Yanzhou Pan, Jiayi Chen, Jiamin Chen, Zhaozhuo Xu, Denghui Zhang

Preference Adaptive and Sequential Text-to-Image Generation
Ofir Nabati, Guy Tennenholtz, ChihWei Hsu, Moonkyung Ryu, Deepak Ramachandran, Yinlam Chow, Xiang Li, Craig Boutilier

Provable Length Generalization in Sequence Prediction via Spectral Filtering
Annie Marsden, Evan Dogariu, Naman Agarwal, Xinyi Chen, Daniel Suo, Elad Hazan

Regression for the Mean: Auto-Evaluation and Inference with Few Labels Through Post-Hoc Regression
Benjamin Eyre*, David Madras

REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective
Simon Geisler, Tom Wollschläger, M. Hesham Abdalla, Vincent Cohen-Addad, Johannes Gasteiger, Stephan Günnemann

Reward-Guided Prompt Evolving in Reinforcement Learning for LLMs
Ziyu Ye, Rishabh Agarwal, Tianqi Liu, Rishabh Joshi, Sarmishta Velury, Quoc V. Le, Qijun Tan, Yuan Liu

Scalable Meta-Learning via Mixed-Mode Differentiation
Iurii Kemaev, Dan A. Calian, Luisa M. Zintgraf, Gregory Farquhar, Hado van Hasselt

SLiM: One-Shot Quantization and Sparsity with Low-Rank Approximation for LLM Weight Compression
Mohammad Mozaffari, Amir Yazdanbakhsh, Maryam Mehri Dehnavi

Synthesizing Privacy-Preserving Text Data via Finetuning Without Finetuning Billion-Scale LLMs
Bowen Tan*, Zheng Xu, Eric Xing, Zhiting Hu, Shanshan Wu

Algorithms and Hardness for Active Learning on Graphs
Vincent Cohen-Addad, Silvio Lattanzi, Simon Meierhans

Best of Both Worlds: Regret Minimization Versus Minimax Play
Adrian Müller, Jon Schneider, Stratis Skoulakis, Luca Viano, Volkan Cevher

The Brain's Bitter Lesson: Scaling Speech Decoding with Self-Supervised Learning
Dulhan Jayalath, Gilad Landau, Brendan Shillingford, Mark Woolrich, ʻŌiwi Parker Jones

Breaking the Quadratic Barrier: Robust Cardinality Sketches for Adaptive Queries
Edith Cohen, Mihir Singhal, Uri Stemmer

Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics
Tyler Kastner, Mark Rowland, Yunhao Tang*, Murat Erdogdu, Amir-massoud Farahmand

Catoni Contextual Bandits Are Robust to Heavy-Tailed Rewards
Chenlu Ye, Yujia Jin*, Alekh Agarwal, Tong Zhang

Correlation Clustering Beyond the Pivot Algorithm
Soheil Behnezhad, Moses Charikar, Vincent Cohen-Addad, Alma Ghafari, Weiyun Ma

Deliberation in Latent Space via Differentiable Cache Augmentation
Luyang Liu, Jonas Pfeiffer, Jiaxing Wu, Jun Xie, Arthur Szlam

Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model
Rachel Cummings, Alessandro Epasto, Jieming Mao, Tamalika Mukherjee, Tingting Ou, Peilin Zhong

Direct Motion Models for Assessing Generated Videos
Kelsey Allen, Carl Doersch, Guangyao Zhou, Mohammed Suhail, Danny Driess, Ignacio Rocco, Yulia Rubanova, Thomas Kipf, Mehdi Sajjadi, Kevin Murphy, João Carreira, Sjoerd van Steenkiste

Epsilon-VAE: Denoising as Visual Decoding
Long Zhao, Sanghyun Woo, Ziyu Wan*, Yandong Li, Han Zhang, Boqing Gong, Hartwig Adam, Xuhui Jia, Ting Liu

Exact Risk Curves of SignSGD in High-Dimensions: Quantifying Preconditioning and Noise-Compression Effects
Kevin Xiao, Noah Marshall, Atish Agarwala, Elliot Paquette

General Agents Need World Models
Jonathan Richens, David Abel, Tom Everitt

Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes
Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett

InfAlign: Inference-Aware Language Model Alignment
Ananth Balashankar, Ziteng Sun, Jonathan Berant, Jacob Eisenstein, Michael Collins, Adrian Hutter, Jong Lee, Chirag Nagpal, Flavien Prost, Aradhana Sinha, Ananda Suresh, Ahmad Beirami

Joint Learning of Energy-based Models and Their Partition Function
Michaël E. Sander, Vincent Roulet, Tianlin Liu, Mathieu Blondel

Loss Functions and Operators Generated by f-Divergences
Vincent Roulet, Tianlin Liu, Nino Vieillard, Michaël E. Sander, Mathieu Blondel

LAuReL: Learned Augmented Residual Layer
Gaurav Menghani, Ravi Kumar, Sanjiv Kumar

Leveraging Per-Instance Privacy for Machine Unlearning
Naz Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel Roy, Gintare Karolina Dziugaite

Nearly Optimal Sample Complexity for Learning with Label Proportions
Robert Busa-Fekete, Travis Dick, Claudio Gentile, Haim Kaplan, Tomer Koren, Uri Stemmer

Positional Attention: Expressivity and Learnability of Algorithmic Computation
Artur Back de Luca, George Giapitzakis, Shenghao Yang, Petar Veličković, Kimon Fountoulakis

Position: Graph Learning Will Lose Relevance Due to Poor Benchmarks
Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Müller, Jan M Tönshoff, Antoine Siraudin, Viktor Zaverkin, Michael Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, Christopher Morris

Position: Stop Treating ‘AGI’ as the North-Star Goal of AI Research
Borhane Blili-Hamelin, Chris Graziul, Leif Hancox-Li, Hananel Hazan, El-Mahdi El-Mhamdi, Avijit Ghosh, Katherine Heller, Jacob Metcalf, Fabricio Murai, Eryk Salvaggio, Andrew Smart, Todd Snider, Mariame Tighanimine, Talia Ringer, Margaret Mitchell, Shiri Dori-Hacohen

Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty
Meera Hahn, Wenjun Zeng, Nithish Kannen, Rich Galt, Kartikeya Badola, Been Kim, Zi Wang

Retrieval Augmented Time Series Forecasting
Sungwon Han, Seungeon Lee, Meeyoung Cha, Sercan Ö. Arik, Jinsung Yoon

Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi

Softmax Is Not Enough (for Sharp Size Generalisation)
Petar Veličković, Christos Perivolaropoulos, Federico Barbero*, Razvan Pascanu

SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity
Samir Khaki, Xiuyu Li, Junxian Guo, Ligeng Zhu, Konstantinos N. Plataniotis, Amir Yazdanbakhsh, Kurt Keutzer, Song Han, Zhijian Liu

Concurrent Reinforcement Learning with Aggregated States via Randomized Least Squares Value Iteration
Yan Chen, Jerry Bai, Yiteng Zhang, Maria Dimakopoulou, Shi Dong, Qi Sun, Zhengyuan Zhou

Convergence of Policy Mirror Descent Beyond Compatible Function Approximation
Uri Sherman, Tomer Koren, Yishay Mansour

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
Xun Wang, Jing Xu, Franziska Boenisch, Michael Backes, Christopher A. Choquette-Choo, Adam Dziedzic

EVOLvE: Evaluating and Optimizing LLMs for In-Context Exploration
Allen Nie, Yi Su, Bo Chang, Jonathan N. Lee, Ed H. Chi, Quoc V. Le, Minmin Chen

Geometric Median (GM) Matching for Robust k-Subset Selection from Noisy Data
Anish Acharya, Sujay Sanghavi, Alex Dimakis, Inderjit Dhillon

Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks
Yuhang Cai, Kangjie Zhou, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett

Interpreting the Repeated Token Phenomenon in Large Language Models
Itay Yona, Jamie Hayes, Ilia Shumailov, Yossi Gandelsman

Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures
Alina Ene, Alessandro Epasto, Vahab Mirrokni, Hoai-An Nguyen, Huy Nguyen, David P. Woodruff, Peilin Zhong

Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
Shangbin Feng, Zifeng Wang, Yike Wang, Sayna Ebrahimi, Hamid Palangi, Lesly Miculicich, Achin Kulshrestha, Nathalie Rauschmayr, Yejin Choi, Yulia Tsvetkov, Chen-Yu Lee, Tomas Pfister

MONA: Myopic Optimization with Non-Myopic Approval Can Mitigate Multi-Step Reward Hacking
Sebastian Farquhar, Vikrant Varma, David Lindner, David Elson, Caleb Biddulph, Ian Goodfellow, Rohin Shah

On Teacher Hacking in Language Model Distillation
Daniil Tiapkin*, Daniele Calandriello, Johan Ferret, Sarah Perrin, Nino Vieillard, Alexandre Ramé, Mathieu Blondel

Rejecting Hallucinated State Targets During Planning
Mingde Zhao, Tristan Sylvain, Romain Laroche, Doina Precup, Yoshua Bengio

Scaling Laws for Differentially Private Language Models
Ryan McKenna, Yangsibo Huang, Amer Sinha, Borja Balle, Zachary Charles, Christopher A. Choquette-Choo, Badih Ghazi, Georgios Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, Chiyuan Zhang

Towards Flexible Perception with Visual Memory
Robert Geirhos, Priyank Jaini, Austin Stone, Sourabh Medapati, Xi Yi, George Toderici, Abhijit Ogale, Jonathon Shlens