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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

03 Oct 2024
Bioeureka and Mila join forces to accelerate AI-assisted pathogen identification
logo Mila and Bioeureka
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01 Oct 2024
Mila Welcomes Material as a New Partner to Drive AI Innovation in Public Company Reporting and Compliance
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26 Sep 2024
Mila's Yoshua Bengio, Alondra Nelson and many other AI experts, put forward the Manhattan Declaration
Participants at a UN meeting in September 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
Combining supervised learning and local search for the multicommodity capacitated fixed-charge network design problem
Charly Robinson La Rocca
Jean-François Cordeau
Emma Frejinger
The multicommodity capacitated fixed-charge network design problem has been extensively studied in the literature due to its wide range of a… (see more)pplications. Despite the fact that many sophisticated solution methods exist today, finding high-quality solutions to large-scale instances remains challenging. In this paper, we explore how a data-driven approach can help improve upon the state of the art. By leveraging machine learning models, we attempt to reveal patterns hidden in the data that might be difficult to capture with traditional optimization methods. For scalability, we propose a prediction method where the machine learning model is called at the level of each arc of the graph. We take advantage of off-the-shelf models trained via supervised learning to predict near-optimal solutions. Our experimental results include an algorithm design analysis that compares various integration strategies of predictions within local search algorithms. We benchmark the ML-based approach with respect to the state-of-the-art heuristic for this problem. The findings indicate that our method can outperform the leading heuristic on sets of instances sampled from a uniform distribution.
2024-12-01
Transportation Research Part E: Logistics and Transportation Review (published)
doi.org
arxiv.org
SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals
Davide Borra
Francesco Paissan
Mirco Ravanelli
2024-11-01
Computers in Biology and Medicine (published)
doi.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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