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Inspiring the development of artificial intelligence for the benefit of all 

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Located in the heart of Quebec’s AI ecosystem, Mila is a community of more than 1,200 researchers specializing in machine learning and dedicated to scientific excellence and innovation.

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News

28 Nov 2024
Congratulations to the winners and finalists of Mila’s first science communication contest!
The three winners of Mila's scientific communication contest.
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26 Nov 2024
Mila, CEIMIA and GPAI launch a groundbreaking report and policy guide on gender equality and diversity in AI
Visual of the GPAI report and guide
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12 Nov 2024
Rising to the occasion : Mila unveils its 2023-2024 Impact Report
Impact report 2023-2024
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Faculty 

Founded in 1993 by Professor Yoshua Bengio, Mila today brings together over 140 professors affiliated with Université de Montréal, McGill University, Polytechnique Montréal and HEC Montréal. Mila also welcomes professors from Université Laval, Université de Sherbrooke, École de technologie supérieure (ÉTS) and Concordia University. 

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Latest Publications

A stochastic integer programming approach to reserve staff scheduling with preferences
Carl Perreault‐Lafleur
Margarida Carvalho
Guy Desaulniers
2025-01-01
Int. Trans. Oper. Res. (published)
doi.org
arxiv.org
Ex Post Conditions for the Exactness of Optimal Power Flow Conic Relaxations
Jean-Luc Lupien
Antoine Lesage-Landry
Convex relaxations of the optimal power flow (OPF) problem provide an efficient alternative to solving the intractable alternating current (… (see more)AC) optimal power flow. The conic subset of OPF convex relaxations, in particular, greatly accelerate resolution while leading to high-quality approximations that are exact in several scenarios. However, the sufficient conditions guaranteeing exactness are stringent, e.g., requiring radial topologies. In this short communication, we present two equivalent ex post conditions for the exactness of any conic relaxation of the OPF. These rely on obtaining either a rank-1 voltage matrix or self-coherent cycles. Instead of relying on sufficient conditions a priori, satisfying one of the presented ex post conditions acts as an exactness certificate for the computed solution. The operator can therefore obtain an optimality guarantee when solving a conic relaxation even when a priori exactness requirements are not met. Finally, we present numerical examples from the MATPOWER library where the ex post conditions hold even though the exactness sufficient conditions do not, thereby illustrating the use of the conditions.
2025-01-01
Electric Power Systems Research (published)
doi.org
arxiv.org
Generalization Limits of Graph Neural Networks in Identity Effects Learning
Giuseppe Alessio D'inverno
Simone Brugiapaglia
Mirco Ravanelli
Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a … (see more)message-passing mechanism and have gained increasing popularity for their intuitive formulation, which is closely linked to the Weisfeiler-Lehman (WL) test for graph isomorphism to which they have been proven equivalent in terms of expressive power. In this work, we establish new generalization properties and fundamental limits of GNNs in the context of learning so-called identity effects, i.e., the task of determining whether an object is composed of two identical components or not. Our study is motivated by the need to understand the capabilities of GNNs when performing simple cognitive tasks, with potential applications in computational linguistics and chemistry. We analyze two case studies: (i) two-letters words, for which we show that GNNs trained via stochastic gradient descent are unable to generalize to unseen letters when utilizing orthogonal encodings like one-hot representations; (ii) dicyclic graphs, i.e., graphs composed of two cycles, for which we present positive existence results leveraging the connection between GNNs and the WL test. Our theoretical analysis is supported by an extensive numerical study.
2025-01-01
Neural Networks (published)
doi.org
arxiv.org
Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study
Mehil B. Shah
Mohammad Masudur Rahman
Foutse Khomh
2025-01-01
Empir. Softw. Eng. (published)
doi.org
arxiv.org
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AI for Humanity

Socially responsible and beneficial development of AI is a fundamental component of Mila’s mission. As a leader in the field, we wish to contribute to social dialogue and the development of applications that will benefit society.

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