Doina Precup

Sumana Basu

Collaborating Alumni - McGill University

Co-supervisor :

Adriana Romero Soriano

Collaborating Alumni - McGill University

Lynn Cherif

Collaborating Alumni - McGill University

Co-supervisor :

PhD - McGill University

Co-supervisor :

PhD - McGill University

Principal supervisor :

David Meger

Jonathan Colaço Carr

Master's Research - McGill University

Principal supervisor :

Prakash Panangaden

Élodie Coté-Gauthier

Collaborating researcher - McGill University

Co-supervisor :

Isabeau Prémont-Schwarz

Franco Del Balso

Collaborating researcher - Université de Montréal

Jesse Farebrother

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

PhD - McGill University

Principal supervisor :

Collaborating researcher - Birla Institute of Technology

Howard Huang

PhD - McGill University

Haque Ishfaq

Collaborating Alumni - McGill University

Mohammad Sami Nur Islam Islam

Master's Research - McGill University

Arushi Jain

Collaborating Alumni - McGill University

PhD - Polytechnique Montréal

Martin Klissarov

PhD - McGill University

Postdoctorate - McGill University

Jonathan Lebensold

Collaborating Alumni - McGill University

Collaborating Alumni - McGill University

Ray Luo

PhD - McGill University

Principal supervisor :

G McCracken

PhD - McGill University

Nazanin Mohammadi Sepahvand

Collaborating Alumni - McGill University

Shahrad Mohammadzadeh

Master's Research - McGill University

Principal supervisor :

Collaborating researcher - McGill University

Co-supervisor :

Irina Rish

Padideh Nouri

PhD - Université de Montréal

Co-supervisor :

PhD - McGill University

Co-supervisor :

Nate Rahn

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

Sahand Rezaei-Shoshtari

PhD - McGill University

Co-supervisor :

PhD - McGill University

Co-supervisor :

PhD - McGill University

Co-supervisor :

PhD - McGill University

Nishanth Anand Vemgal

PhD - McGill University

PhD - McGill University

Co-supervisor :

Samira Ebrahimi Kahou

Zihan Wang

PhD - McGill University

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Guangyuan Wang

Research Intern - McGill University

Steve Wen

Master's Research - McGill University

Co-supervisor :

Gregory Dudek

Zijing Wu

PhD - McGill University

Principal supervisor :

PhD - McGill University

Harry Zhao

Collaborating Alumni - McGill University

Co-supervisor :

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

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Publications

Detoxifying LLMs via Representation Erasure-Based Preference Optimization

Nazanin Mohammadi Sepahvand

Eleni Triantafillou

Hugo Larochelle

Gintare Karolina Dziugaite

Daniel M. Roy

Large language models (LLMs) trained on webscale data can produce toxic outputs, raising concerns for safe deployment. Prior defenses, based… (see more) on applications of DPO, NPO, and similar algorithms, reduce the likelihood of harmful continuations, but not robustly so: they are vulnerable to adversarial prompting and easily undone by fine-tuning-based relearning attacks. Indeed, research has shown that these edits to the model are superficial: linear probing reveals that harmful "directions" remain present in representations. To address this, we propose Representation Erasure-based Preference Optimization (REPO), reformulating detoxification as a token-level preference problem. Using a novel objective with preference data, we force the representations of toxic continuations to converge toward their benign counterparts. Our mechanistic analysis reveals that this granular approach is critical: unlike baselines, REPO induces deep, localized edits to toxicity-encoding neurons while preserving general model utility. Exhaustive evaluations show that REPO achieves state-of-the-art robustness, stopping sophisticated threats-including relearning attacks and enhanced GCG jailbreaks-where existing representation- and output-based methods fail.

2026-02-23

arXiv (preprint)

Fluid-Agent Reinforcement Learning

Shishir Sharma

Theodore J. Perkins

The primary focus of multi-agent reinforcement learning (MARL) has been to study interactions among a fixed number of agents embedded in an … (see more)environment. However, in the real world, the number of agents is neither fixed nor known a priori. Moreover, an agent can decide to create other agents (for example, a cell may divide, or a company may spin off a division). In this paper, we propose a framework that allows agents to create other agents; we call this a fluid-agent environment. We present game-theoretic solution concepts for fluid-agent games and empirically evaluate the performance of several MARL algorithms within this framework. Our experiments include fluid variants of established benchmarks such as Predator-Prey and Level-Based Foraging, where agents can dynamically spawn, as well as a new environment we introduce that highlights how fluidity can unlock novel solution strategies beyond those observed in fixed-population settings. We demonstrate that this framework yields agent teams that adjust their size dynamically to match environmental demands.

2026-02-15

ArXiv (preprint)

Pregnancy AI: Development and Internal Validation of an Artificial Intelligence Tool to Predict Live Births in ICSI and IVF Cycles Using Clinical Features and Embryo Images

Jaume Minano Masip

Penelope Borduas

Isaac-Jacques Kadoch

Simon Phillips

Daniel Dufort

2026-02-11

Medicina (published)

Affordances Enable Partial World Modeling with LLMs

Khimya Khetarpal

Gheorghe Comanici

Jonathan Richens

Jeremy Shar

Fei Xia

Laurent Orseau

Aleksandra Faust

2026-02-10

ArXiv (preprint)

The Geometry and Topology of Circuits: the Manifolds of Modular Addition

Colin Daniels

Jonathan Love

The Clock and Pizza interpretations, associated with architectures differing in either uniform or learnable attention, were introduced to ar… (see more)gue that different architectural designs can yield distinct circuits for modular addition. In this work, we show that this is not the case, and that both the uniform and trainable attention architectures implement the same algorithm via topologically and geometrically equivalent representations. Our methodology goes beyond the interpretation of individual neurons and weights. Instead, we identify all of the neurons corresponding to each learned representation and then study the collective group of neurons as one entity. This method reveals that each learned representation is a manifold that we can study utilizing tools from topology. Based on this insight, we can statistically analyze the learned representations across hundreds of circuits to demonstrate the similarity between learned modular addition circuits that arise naturally from common deep learning paradigms.

2025-12-31

International Conference on Learning Representations (Accept (Poster))

On the geometry and topology of representations: the manifolds of modular addition

Colin Daniels

Jonathan Love

The Clock and Pizza interpretations, associated with architectures differing in either uniform or learnable attention, were introduced to ar… (see more)gue that different architectural designs can yield distinct circuits for modular addition. In this work, we show that this is not the case, and that both uniform attention and trainable attention architectures implement the same algorithm via topologically and geometrically equivalent representations. Our methodology goes beyond the interpretation of individual neurons and weights. Instead, we identify all of the neurons corresponding to each learned representation and then study the collective group of neurons as one entity. This method reveals that each learned representation is a manifold that we can study utilizing tools from topology. Based on this insight, we can statistically analyze the learned representations across hundreds of circuits to demonstrate the similarity between learned modular addition circuits that arise naturally from common deep learning paradigms.

2025-12-30

arXiv (preprint)

A Message from AI Research Leaders: Join Us in Supporting OpenReview

Andrew Y. Ng

Ruslan Salakhutdinov

Fernando Pereira

2025-12-17

OpenReview (unknown)

Large Language Model Applications in the Algebra Domain: A Systematic Review

Yajie Song

Yimei Zhang

Reihaneh Rabbany

Maria Cutumisu

2025-12-05

Technology, Knowledge and Learning (published)

On Mobile Ad Hoc Networks for Coverage of Partially Observable Worlds

Edwin Meriaux

Shuo Wen

Louis-Roy Langevin

Antonio Lor'ia

Gregory Dudek

2025-11-30

arXiv (published)

Excitatory-Inhibitory Dynamics in Adaptive Decision-Making

Veronica Chelu

2025-11-15

Lecture Notes in Computer Science (published)

The Geometry and Topology of Modular Addition Representations

Colin Daniels

Jonathan Love

The Clock and Pizza interpretations, associated with neural architectures differing in either uniform or learnable attention, were introduce… (see more)d to argue that different architectural designs can yield distinct circuits for modular addition. Applying geometric and topological analyses to learned representations, we show that this is not the case: Clock and Pizza circuits are topologically and geometrically equivalent and are thus equivalent representations.

2025-11-12

TAG-DS/2025/Conference (poster)

Reward the Reward Designer: Making Reinforcement Learning Useful for Clinical Decision Making

Sumana Basu

Adriana Romero

2025-09-21

NeurIPS.cc/2025/Workshop/WiML (published)