Doina Precup

Samin Yeasar Arnob

PhD - McGill University

arnobsam@mila.quebec

sumana.basu@mail.mcgill.ca

Sumana Basu

Collaborating Alumni - McGill University

Co-supervisor :

Adriana Romero Soriano

Master's Research - McGill University

gunbir-singh.baveja@mila.quebec

Collaborating Alumni - McGill University

cheluver@mila.quebec

Raymond Chua

PhD - McGill University

Co-supervisor :

PhD - McGill University

Principal supervisor :

David Meger

chungwes@mila.quebec

Jonathan Colaço Carr

Master's Research - McGill University

Principal supervisor :

Prakash Panangaden

jonathan.colaco-carr@mila.quebec

Élodie Coté-Gauthier

Master's Research - McGill University

Co-supervisor :

Isabeau Prémont-Schwarz

elodie.cote-gauthier@mila.quebec

Franco Del Balso

Master's Research - McGill University

franco.delbalso@mila.quebec

busra-tugce.gurbuz@mila.quebec

Jesse Farebrother

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

PhD - McGill University

Principal supervisor :

Eilif B. Muller

Mohammad Sami Nur Islam Islam

Jonathan Hu

Master's Research - McGill University

jonathan.hu@mila.quebec

Howard Huang

PhD - McGill University

huanghow@mila.quebec

Master's Research - McGill University

mohammad-sami-nur.islam@mila.quebec

Hangzhan Jin

PhD - Polytechnique Montréal

jinhangzhan@gmail.com

flemming.kondrup@mila.quebec

Flemming Kondrup

Postdoctorate - McGill University

Jonathan Lebensold

Collaborating Alumni - McGill University

Collaborating Alumni - McGill University

jeremy.lucas@mila.quebec

Ray Luo

PhD - McGill University

Principal supervisor :

Xujie Si

luo.ziyan@mila.quebec

Sicheng Lyu

Master's Research - McGill University

sicheng.lyu@mila.quebec

G McCracken

PhD - McGill University

mccrackg@mila.quebec

shahrad.mohammadzadeh@mila.quebec

Shahrad Mohammadzadeh

Master's Research - McGill University

Principal supervisor :

Reihaneh Rabbany

padideh.nouri@mila.quebec

Collaborating researcher - McGill University

Co-supervisor :

Irina Rish

moisescg@mila.quebec

Padideh Nouri

PhD - Université de Montréal

PhD - McGill University

Co-supervisor :

Nate Rahn

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

nathan.rahn@mila.quebec

Manoosh Samiei

PhD - McGill University

Co-supervisor :

Paul Masset

manoosh.samiei@mila.quebec

PhD - McGill University

Co-supervisor :

Blake Richards

samiemandana@gmail.com

PhD - McGill University

Nishanth Anand Vemgal

PhD - McGill University

PhD - McGill University

Co-supervisor :

Samira Ebrahimi Kahou

Research Intern - McGill University

guangyuan.wang@mila.quebec

Zihan Wang

PhD - McGill University

zihan.wang@mila.quebec

william.potter@mila.quebec

Steve Wen

Master's Research - McGill University

Co-supervisor :

Gregory Dudek

steve.wen@mila.quebec

Will William

Research Intern - McGill University

shuyuan.zhang@mila.quebec

Zijing Wu

PhD - McGill University

Principal supervisor :

Paul Masset

zijing.wu@mila.quebec

PhD - McGill University

Harry Zhao

Collaborating Alumni - McGill University

Co-supervisor :

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

Read the article

Publications

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)

doi.org

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)

arxiv.org

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.

2026-01-25

International Conference on Learning Representations (poster)

doi.org

Deep neural networks divide and conquer dihedral multiplication

Sihui Wei

Gavin McCracken

Harley Wiltzer

Irina Rish

Jonathan Love

We find multilayer perceptrons and transformers both universally learn an instantiation of the same divide-and-conquer algorithm that requir… (see more)es only a logarithmic number of neural representations to solve dihedral multiplication. Clustering neurons based on similar activation behaviour reveals remarkably clear structure: each neural representation corresponds to a Cayley graph. To our knowledge, this is the first work that fully characterizes and describes all neural representations that are learnable on a dataset, while prior work on group multiplications studied neuron-level behavior, or preliminarily investigated cluster behavior. Thus, we can understand the algorithm networks universally learn at three levels of abstraction: 1) Neurons activate on coset or approximate coset structure of the dihedral group. 2) Groups of neurons together form neural representations that act to divide the dataset into different subproblems, being Cayley graphs, where the equivalence class of the answer is computed. 3) The global algorithm then linearly combines each neural representation (subproblem) together at the logits. This work provides a deep case study and provides the community with a very well understood toy model for interpretability, as well as makes steps toward proving the conjecture that DNNs will divide and conquer all group multiplication tasks.

2025-12-31

International Conference on Machine Learning (Accept (regular))

proceedings.mlr.press

Learning from Pairwise Preferences in Long-Term Decision Problems

Jonathan Colaço Carr

Prakash Panangaden

Benjamin Van Roy

Agents that can beat or tie any other under a model of pairwise preference have strong guarantees for both user satisfaction and overall soc… (see more)ial welfare. However, searching for these agents in long-term decision problems is not computationally tractable with current approaches, which require the size of an agent's policy to increase with the problem length. We introduce the \textit{Markov decision contest}, a model of learning from general preferences in long-term (infinite-horizon) decision problems. Within this model, we prove that agents only need a stationary Markov policy in order to be optimal (that is, to beat or tie any agent with a history-dependent policy); that the problem of finding an optimal policy is in P; and that a simple iterative algorithm (which we call Hedged Policy Iteration) converges to an optimal policy at a sublinear rate. In a suite of high-dimensional experiments, we demonstrate that Hedged Policy Iteration scales well to function approximation. Lastly, we present a near approximation of Hedged Policy Iteration, called HPI-Clip, which both matches the performance of Proximal Policy Optimization on reward-based tasks while also outperforming it on tasks with non-transitive preferences. These results show that learning from pairwise preferences in long-term decision problems can be far more tractable than what is known from prior work.

2025-12-31

International Conference on Machine Learning (Accept (regular))

Permanent and Transient Representations for Continual Reinforcement Learning

Nishanth Anand

2025-12-31

Neural Information Processing Systems (Accept (poster))

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))

The Prestige: Benchmarking Cognitive Visual Reasoning using Magic Tricks

Shuo Wen

Beixi Du

Edwin Meriaux

Junming Shi

Chloe Si

David Meger

Gregory Dudek

2025-12-31

Neural Information Processing Systems (Accept (poster))

Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments

Ziyan "Ray" Luo

A key approach to state abstraction is approximating behavioral metrics (notably, bisimulation metrics) in the observation space and embeddi… (see more)ng these learned distances in the representation space. While promising for robustness to task-irrelevant noise, as shown in prior work, accurately estimating these metrics remains challenging, requiring various design choices that create gaps between theory and practice. Prior evaluations focus mainly on final returns, leaving the quality of learned metrics and the source of performance gains unclear. To systematically assess how metric learning works in deep reinforcement learning (RL), we evaluate five recent approaches, unified conceptually as isometric embeddings with varying design choices. We benchmark them with baselines across 20 state-based and 14 pixel-based tasks, spanning 370 task configurations with diverse noise settings. Beyond final returns, we introduce the evaluation of a denoising factor to quantify the encoder's ability to filter distractions. To further isolate the effect of metric learning, we propose and evaluate an isolated metric estimation setting, in which the encoder is influenced solely by the metric loss. Finally, we release an open-source, modular codebase to improve reproducibility and support future research on metric learning in deep RL.

2025-12-30

OpenReview (published)

doi.org