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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

27 Oct 2025
AI Researcher Yoshua Bengio Becomes First Living Scientist to Reach 1 Million Citations on Google Scholar
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21 Oct 2025
Aishwarya Agrawal awarded Everingham Prize 2025
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17 Oct 2025
BDC and Mila renew their partnership to support innovation and the competitiveness of Canadian SMEs with artificial intelligence
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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

The role of Large Language Models in IoT security: A systematic review of advances, challenges, and opportunities
Saeid Jamshidi
Negar Shahabi
Amin Nikanjam
Kawser Wazed Nafi
Foutse Khomh
Carol Fung
2025-11-01
Internet of Things (published)
doi.org
Random Forest Autoencoders for Guided Representation Learning
Adrien Aumon
Shuang Ni
Myriam Lizotte
Guy Wolf
Kevin R. Moon
Jake S. Rhodes
Decades of research have produced robust methods for unsupervised data visualization, yet supervised visualization…
2025-10-22
logconference.io/LOG/2025/Conference (poster)
doi.org
openreview.net
The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies
Eirini Angeloudi
Marc Huertas-Company
Jesús Falcón-Barroso
Laurence Perreault-Levasseur
Alexandre Adam
Alina Boecker
2025-10-21
Astronomy & Astrophysics (published)
doi.org
arxiv.org
Improved Localized Machine Unlearning Through the Lens of Memorization
Reihaneh Torkzadehmahani
Reza Nasirigerdeh
Georgios Kaissis
Daniel Rueckert
Gintare Karolina Dziugaite
Eleni Triantafillou
Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after… (see more) it has already been trained. This is important for key applications, including making the model more accurate by removing outdated, mislabeled, or poisoned data. In this work, we study localized unlearning, where the unlearning algorithm operates on a (small) identified subset of parameters. Drawing inspiration from the memorization literature, we propose an improved localization strategy that yields strong results when paired with existing unlearning algorithms. We also propose a new unlearning algorithm, Deletion by Example Localization (DEL), that resets the parameters deemed-to-be most critical according to our localization strategy, and then finetunes them. Our extensive experiments on different datasets, forget sets and metrics reveal that DEL sets a new state-of-the-art for unlearning metrics, against both localized and full-parameter methods, while modifying a small subset of parameters, and outperforms the state-of-the-art localized unlearning in terms of test accuracy too.
2025-10-20
TMLR (accepted)
doi.org
openreview.net
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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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