Accueil

Inspirer le développement de l'intelligence artificielle au bénéfice de tous·tes

Un professeur s'entretient avec ses étudiants dans un café/lounge.

Situé au cœur de l’écosystème québécois en intelligence artificielle (IA), Mila rassemble une communauté de plus de 1200 personnes spécialisées en apprentissage automatique et dédiées à l’excellence scientifique et l’innovation.

À propos

À la une
À la une
À la une

Nouvelles

01 Mai 2025
Suivez les chercheuses et chercheurs de Mila à AISTATS 2025
Logo Mila et AISTATS
Lire la nouvelle
29 Avr 2025
Mila Techaide 2025 recueille plus de 110 000$ de dons
Photo de David Adelani s'exprimant devant un public à Mila
Lire la nouvelle
24 Avr 2025
Innovasea devient partenaire de Mila pour faire progresser l'innovation en matière d’IA dans l'économie bleue
logo Mila et Innovasea
Lire la nouvelle
Voir plus de nouvelles

Corps professoral

Fondé en 1993 par le professeur Yoshua Bengio, Mila regroupe aujourd'hui plus de 140 professeur·e·s affilié·e·s à l'Université de Montréal, l'Université McGill, Polytechnique Montréal et HEC Montréal. L'institut accueille également des professeur·e·s de l'Université Laval, de l'Université de Sherbrooke, de l'École de technologie supérieure (ÉTS) et de l'Université Concordia.

Consultez l'annuaire en ligne

Photo de Yoshua Bengio

Publications récentes

Evaluating machine learning-driven intrusion detection systems in IoT: Performance and energy consumption
Saeid Jamshidi
Kawser Wazed Nafi
Amin Nikanjam
Foutse Khomh
2025-06-01
Computers & Industrial Engineering (publié)
doi.org
arxiv.org
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent
Yewon Lee
Andrew Zou Li
Yizhou Huang
Philip Huang
Eric Heiden
Krishna Murthy
Fabian Damken
Kevin A. Smith
Derek Nowrouzezahrai
Fabio Ramos
Florian Shkurti
Carnegie-mellon University
M. I. O. Technology
Technische Universitat Darmstadt
Nvidia
M. University
University of Sydney
Planning for many manipulation tasks, such as using tools or assembling parts, often requires both symbolic and geometric reasoning. Task an… (voir plus)d Motion Planning (TAMP) algorithms typically solve these problems by conducting a tree search over high-level task sequences while checking for kinematic and dynamic feasibility. While performant, most existing algorithms are highly inefficient as their time complexity grows exponentially with the number of possible actions and objects. Additionally, they only find a single solution to problems in which many feasible plans may exist. To address these limitations, we propose a novel algorithm called Stein Task and Motion Planning (STAMP) that leverages parallelization and differentiable simulation to efficiently search for multiple diverse plans. STAMP relaxes discrete-and-continuous TAMP problems into continuous optimization problems that can be solved using variational inference. Our algorithm builds upon Stein Variational Gradient Descent, a gradient-based variational inference algorithm, and parallelized differentiable physics simulators on the GPU to efficiently obtain gradients for inference. Further, we employ imitation learning to introduce action abstractions that reduce the inference problem to lower dimensions. We demonstrate our method on two TAMP problems and empirically show that STAMP is able to: 1) produce multiple diverse plans in parallel; and 2) search for plans more efficiently compared to existing TAMP baselines.
2025-06-01
IEEE Robotics and Automation Letters (publié)
doi.org
openreview.net
Mitigating Goal Misgeneralization via Minimax Regret
Karim Ahmed Abdel Sadek
Matthew Farrugia-Roberts
Hannah Erlebach
Christian Schroeder de Witt
David Scott Krueger
Usman Anwar
Michael D Dennis
Robustness research in reinforcement learning often focuses on ensuring that the policy consistently exhibits capable, goal-driven behavior.… (voir plus) However, not every capable behavior is the intended behavior. *Goal misgeneralization* can occur when the policy generalizes capably with respect to a 'proxy goal' whose optimal behavior correlates with the intended goal on the training distribution, but not out of distribution. Though the intended goal would be ambiguous if they were perfectly correlated in training, we show progress can be made if the goals are only *nearly ambiguous*, with the training distribution containing a small proportion of *disambiguating* levels. We observe that the training signal from disambiguating levels could be amplified by regret-based prioritization. We formally show that approximately optimal policies on maximal-regret levels avoid the harmful effects of goal misgeneralization, which may exist without this prioritization. Empirically, we find that current regret-based Unsupervised Environment Design (UED) methods can mitigate the effects of goal misgeneralization, though do not always entirely eliminate it. Our theoretical and empirical results show that as UED methods improve they could further mitigate goal misgeneralization in practice.
2025-05-09
rl-conference.cc/RLC/2025/Conference (accepté)
openreview.net
openreview.net
"It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large Language Models
Angel Hsing-Chi Hwang
Q. Vera Liao
Su Lin Blodgett
Alexandra Olteanu
Adam Trischler
2025-05-02
Proceedings of the ACM on Human-Computer Interaction (publié)
doi.org
arxiv.org
Voir plus de publications

IA pour l'humanité

Le développement socialement responsable et bénéfique de l'IA est une dimension fondamentale de la mission de Mila. En tant que chef de file, nous souhaitons contribuer au dialogue social et au développement d'applications qui seront bénéfiques pour la société.

En savoir plus

Une personne regarde un ciel étoilé.