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

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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.

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Nouvelles

08 Juil 2025
AI4Good Lab 2025 : 14 projets d'IA pour un réel impact social
PHoto de groupe de la cohorte 2025 du AI4Good Lab 2025
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08 Juil 2025
Quandela et Mila s’associent pour développer des technologies hybrides combinant intelligence artificielle et informatique quantique
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03 Juil 2025
Cohere devient partenaire de Mila et ouvre un bureau à Montréal
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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.

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Publications récentes

Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
In Kidney Exchange Programs (KEPs), each participating patient is registered together with an incompatible donor. Donors without an incompat… (voir plus)ible patient can also register. Then, KEPs typically maximize overall patient benefit through donor exchanges. This aggregation of benefits calls into question potential individual patient disparities in terms of access to transplantation in KEPs. Considering solely this utilitarian objective may become an issue in the case where multiple exchange plans are optimal or near-optimal. In fact, current KEP policies are all-or-nothing, meaning that only one exchange plan is determined. Each patient is either selected or not as part of that unique solution. In this work, we seek instead to find a policy that contemplates the probability of patients of being in a solution. To guide the determination of our policy, we adapt popular fairness schemes to KEPs to balance the usual approach of maximizing the utilitarian objective. Different combinations of fairness and utilitarian objectives are modelled as conic programs with an exponential number of variables. We propose a column generation approach to solve them effectively in practice. Finally, we make an extensive comparison of the different schemes in terms of the balance of utility and fairness score, and validate the scalability of our methodology for benchmark instances from the literature.
2025-08-01
European Journal of Operational Research (publié)
doi.org
arxiv.org
Assemblies, synapse clustering, and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
András Ecker
Daniela Egas Santander
Marwan Abdellah
Jorge Blanco Alonso
Sirio Bolaños-Puchet
Giuseppe Chindemi
Dhuruva Priyan Gowri Mariyappan
James B. Isbister
James King
Pramod Kumbhar
Ioannis Magkanaris
Eilif Benjamin Muller
Michael W. Reimann
Synaptic plasticity underlies the brain’s ability to learn and adapt. While experiments in brain slices have revealed mechanisms and proto… (voir plus)cols for the induction of plasticity between pairs of neurons, how these synaptic changes are coordinated in biological neuronal networks to ensure the emergence of learning remains poorly understood. Simulation and modeling have emerged as important tools to study learning in plastic networks, but have yet to achieve a scale that incorporates realistic network structure, active dendrites, and multi-synapse interactions, key determinants of synaptic plasticity. To rise to this challenge, we endowed an existing large-scale cortical network model, incorporating data-constrained dendritic processing and multi-synaptic connections, with a calcium-based model of functional plasticity that captures the diversity of excitatory connections extrapolated to in vivo-like conditions. This allowed us to study how dendrites and network structure interact with plasticity to shape stimulus representations at the microcircuit level. In our exploratory simulations, plasticity acted sparsely and specifically, firing rates and weight distributions remained stable without additional homeostatic mechanisms. At the circuit level, we found plasticity was driven by co-firing stimulus-evoked functional assemblies, spatial clustering of synapses on dendrites, and the topology of the network connectivity. As a result of the plastic changes, the network became more reliable with more stimulus-specific responses. We confirmed our testable predictions in the MICrONS datasets, an openly available electron microscopic reconstruction of a large volume of cortical tissue. Our results quantify at a large scale how the dendritic architecture and higher-order structure of cortical microcircuits play a central role in functional plasticity and provide a foundation for elucidating their role in learning.
2025-07-03
eLife (publié)
doi.org
A Geometric Lens on RL Environment Complexity Based on Ricci Curvature
Ali Saheb Pasand
Pablo Samuel Castro
Pouya Bashivan
We introduce Ollivier-Ricci Curvature (ORC) as an information-geometric tool for analyzing the local structure of reinforcement learning (RL… (voir plus)) environments. We establish a novel connection between ORC and the Successor Representation (SR), enabling a geometric interpretation of environment dynamics decoupled from reward signals. Our analysis shows that states with positive and negative ORC values correspond to regions where random walks converge and diverge respectively, which are often critical for effective exploration. ORC is highly correlated with established environment complexity metrics, yet integrates naturally with standard RL frameworks based on SR and provides both global and local complexity measures. Leveraging this property, we propose an ORC-based intrinsic reward that guides agents toward divergent regions and away from convergent traps. Empirical results demonstrate that our curvature-driven reward substantially improves exploration performance across diverse environments, outperforming both random and count-based intrinsic reward baselines.
2025-07-01
rl-conference.cc/RLC/2025/Workshop/RLBrew (publié)
openreview.net
openreview.net
Curiosity-Driven Exploration via Temporal \\ Contrastive Learning
Faisal Mohamed
Catherine Ji
Benjamin Eysenbach
Glen Berseth
Exploration remains a key challenge in reinforcement learning (RL), especially in long-horizon tasks and environments with high-dimensional … (voir plus)observations. A common strategy for effective exploration is to promote state coverage or novelty, which often involves estimating the agent's state visitation distribution. In this paper, we propose \textbf{C}uriosity-Driven Exploration via \textbf{Te}mporal \textbf{C}ontrastive Learning (\methodName), an exploration method based on temporal contrastive learning that rewards agents for reaching states with unexpected futures. This incentivizes uncovering meaningful less-visited states. \methodName is simple and does not require explicit density or uncertainty estimation, while learning representations aligned with the RL objective. It consistently outperforms standard baselines in complex mazes using different embodiments (Ant and Humanoid) and robotic manipulation tasks, while also yielding more diverse behaviors in Craftax without requiring task-specific information.
2025-07-01
rl-conference.cc/RLC/2025/Workshop/RLBrew (publié)
openreview.net
openreview.net
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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.

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