Publications

Multi-Modal Language Models as Text-to-Image Model Evaluators

Jiahui Chen

Candace Ross

Reyhane Askari Hemmat

Koustuv Sinha

Melissa Hall

Michal Drozdzal

Adriana Romero

2025-04-30

arXiv (publié)

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning

Guozheng Ma

Lu Li

Zilin Wang

Li Shen

Pierre-Luc Bacon

Dacheng Tao

Effectively scaling up deep reinforcement learning models has proven notoriously difficult due to network pathologies during training, motiv… (voir plus)ating various targeted interventions such as periodic reset and architectural advances such as layer normalization. Instead of pursuing more complex modifications, we show that introducing static network sparsity alone can unlock further scaling potential beyond their dense counterparts with state-of-the-art architectures. This is achieved through simple one-shot random pruning, where a predetermined percentage of network weights are randomly removed once before training. Our analysis reveals that, in contrast to naively scaling up dense DRL networks, such sparse networks achieve both higher parameter efficiency for network expressivity and stronger resistance to optimization challenges like plasticity loss and gradient interference. We further extend our evaluation to visual and streaming RL scenarios, demonstrating the consistent benefits of network sparsity.

2025-04-30

ICML.cc/2025/Conference (présentation orale)

Outsourced Diffusion Sampling: Efficient Posterior Inference in Latent Spaces of Generative Models

Any well-behaved generative model over a variable …

2025-04-30

International Conference on Machine Learning (poster)

Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind

Mouad Abrini

Omri Abend

Dina M. Acklin

Henny Admoni

Gregor Aichinger

Nitay Alon

Zahra Ashktorab

Ashish Atreja

Moises Auron

Alexander Aufreiter

Raghav Awasthi

Soumya Banerjee

Joseph Barnby

Rhea Basappa

Severin Bergsmann

Djallel Bouneffouf

Patrick Callaghan

Marc Cavazza

Thierry Chaminade

Sonia Chernova … (voir 88 de plus)

Mohamed Chetouan

Moumita Choudhury

Axel Cleeremans

J. Cywinski

Fabio Cuzzolin

Hokin Deng

N'yoma Diamond

C. D. Pasquasio

Guillaume Dumas

Max J. van Duijn

Mahapatra Dwarikanath

Qingying Gao

Ashok Goel

Rebecca R. Goldstein

Matthew C. Gombolay

Gabriel Enrique Gonzalez

Amar Halilovic

Tobias Halmdienst

Mahimul Islam

Julian Jara-Ettinger

Natalie Kastel

Renana Keydar

Ashish K. Khanna

Mahdi Khoramshahi

Jihyun Kim

Mihyeon Kim

Youngbin Kim

Senka Krivic

Nikita Krasnytskyi

Arun Kumar

Junehyoung Kwon

EunJu Lee

Shane Lee

Peter R. Lewis 0001

Xue Li

Yijiang Li

Michal Lewandowski

Nathan Lloyd

Matthew B. Luebbers

Dezhi Luo

Haiyun Lyu

Dwarikanath Mahapatra

Kamal Maheshwari

Mallika Mainali

P. Mathur

Patrick Mederitsch

Shuwa Miura

Manuel Preston de Miranda

Reuth Mirsky

Shreya Mishra

Nina M. Moorman

Katelyn Morrison

John Muchovej

Bernhard Nessler

Felix Nessler

Hieu Minh Jord Nguyen

Abby Ortego

F. Papay

Antoine Pasquali

Hamed Rahimi

C. Raghu

Amanda L. Royka

Stefan Sarkadi

Jaelle Scheuerman

Simon Schmid

Paul Schrater

Anik Sen

Zahra Sheikhbahaee

Ke Shi

Reid G. Simmons

Nishant Singh

Mason O. Smith

Ramira van der Meulen

Anthia Solaki

Haoran Sun

Viktor Szolga

Matthew E. Taylor

Travis Taylor

Sanne van Waveren

Juan David Vargas

R. Verbrugge

Eitan Wagner

Justin D. Weisz

Ximing Wen

William Yeoh

Wenlong Zhang

Michelle Zhao

Shlomo Zilberstein

2025-04-30

arXiv (publié)

Replication of a GWAS signal near HLA-DQA2 with AML using a disease-only cohort and external population-based controls

Rose Laflamme

Véronique Lisi

Josée Hébert

Guy Sauvageau

Sébastien Lemieux

Vincent-Philippe Lavallee

Guillaume Lettre

2025-04-30

Blood Neoplasia (publié)

Scaling Trends in Language Model Robustness

Nikolaus Howe

Ian McKenzie

Oskar Hollinsworth

Michał Zając

Tom Tseng

Aaron Tucker

Pierre-Luc Bacon

Adam Gleave

Increasing model size has unlocked a dazzling array of capabilities in modern language models. At the same time, even frontier models remain… (voir plus) vulnerable to jailbreaks and prompt injections, despite concerted efforts to make them robust. As both attack and defense gain access to more compute, and as models become larger, what happens to robustness? We argue that to answer this question requires a \emph{scaling} approach, which we employ in an extensive study of language model robustness across several classification tasks, model families, and adversarial attacks. We find that in the absence of explicit safety training, larger models are not consistently more robust; however, scale improves sample efficiency in adversarial training, though it worsens compute efficiency. Further, we find that increasing attack compute smoothly improves attack success rate against both undefended and adversarially trained models. Finally, after exploring robustness transfer across attacks and threat models, we combine attack and defense scaling rates to study the offense-defense balance. We find that while attack scaling outpaces adversarial training across all models studied, larger adversarially trained models might give defense the advantage in the long run. These results underscore the utility of the scaling lens, and provide a paradigm for evaluating future attacks and defenses on frontier models.

2025-04-30

ICML.cc/2025/Conference (poster)

SCAR: Shapley Credit Assignment for More Efficient RLHF

Meng Cao

Shuyuan Zhang

Xiao-Wen Chang

Doina Precup

2025-04-30

arXiv (publié)

SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs

Roozbeh Aghili

Xingfang Wu

Foutse Khomh

Heng Li

2025-04-30

arXiv (publié)

Seeing the Unseen: How EMoE Unveils Bias in Text-to-Image Diffusion Models

Lucas Berry

Axel Brando

Wei-Di Chang

Juan Higuera

David Meger

2025-04-30

arXiv (publié)

openreview.net

Self-Evolving Curriculum for LLM Reasoning

Nicolas Gontier

Ehsan Kamalloo

2025-04-30

arXiv (publié)

Self-Play Q-Learners Can Provably Collude in the Iterated Prisoner's Dilemma

Quentin Bertrand

Juan Agustin Duque

Emilio Calvano

Gauthier Gidel

A growing body of computational studies shows that simple machine learning agents converge to cooperative behaviors in social dilemmas, such… (voir plus) as collusive price-setting in oligopoly markets, raising questions about what drives this outcome. In this work, we provide theoretical foundations for this phenomenon in the context of self-play multi-agent Q-learners in the iterated prisoner’s dilemma. We characterize broad conditions under which such agents provably learn the cooperative Pavlov (win-stay, lose-shift) policy rather than the Pareto-dominated “always defect” policy. We validate our theoretical results through additional experiments, demonstrating their robustness across a broader class of deep learning algorithms.

2025-04-30

ICML.cc/2025/Conference (poster)