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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

04 Feb 2025
Mila invests in a sustainable future with new Sustainability Scholarship Program
Banner with the name of the program Sustainability Scholarship Program
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30 Jan 2025
Spotlight on our alumni: Desislava Aleksandrova
Picture of Desislava Aleksandrova
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29 Jan 2025
Launch of the First International Report on AI Safety chaired by Yoshua Bengio
Visual identity of the international report on AI safety.
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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 BrowserGym Ecosystem for Web Agent Research
Thibault Le Sellier de Chezelles
Maxime Gasse
Alexandre Lacoste
Massimo Caccia
Alexandre Drouin
Léo Boisvert
Megh Thakkar
Tom Marty
Rim Assouel
Sahar Omidi Shayegan
Lawrence Keunho Jang
Xing Han Lu
Ori Yoran
Dehan Kong
Frank F. Xu
Siva Reddy
Graham Neubig
Quentin Cappart
Russ Salakhutdinov
Nicolas Chapados
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging a… (see more)utomation and Large Language Models (LLMs) for web interaction tasks. Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. BrowserGym aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. Combined with AgentLab, a complementary framework that aids in agent creation, testing, and analysis, BrowserGym offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. This standardized approach seeks to reduce the time and complexity of developing web agents, supporting more reliable comparisons and facilitating in-depth analysis of agent behaviors, and could result in more adaptable, capable agents, ultimately accelerating innovation in LLM-driven automation. As a supporting evidence, we conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across all benchmarks currently available in BrowserGym. Among other findings, our results highlight a large discrepancy between OpenAI and Anthropic's latests models, with Claude-3.5-Sonnet leading the way on almost all benchmarks, except on vision-related tasks where GPT-4o is superior. Despite these advancements, our results emphasize that building robust and efficient web agents remains a significant challenge, due to the inherent complexity of real-world web environments and the limitations of current models.
2025-03-08
TMLR (accepted)
doi.org
openreview.net
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard
Chandler Squires
Jonas Wahl
Konrad Paul Kording
Karen Sachs
Alexandre Drouin
Dhanya Sridhar
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientifi… (see more)c disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.
2025-03-05
ICLR.cc/2025/Workshop/MLDPR (published)
doi.org
openreview.net
Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection
Eslam G. Al-Sakkari
Ahmed Ragab
Mostafa Amer
Olumoye Ajao
Marzouk Benali
Daria C. Boffito
Hanane Dagdougui
Mouloud Amazouz
2025-03-01
Digital Chemical Engineering (published)
doi.org
Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 -- Ex vivo imaging: data processing, comparisons with microscopy, and tractography
Kurt G Schilling
Amy F D Howard
Francesco Grussu
Andrada Ianus
Brian Hansen
Rachel L. C. Barrett
Manisha Aggarwal
Stijn Michielse
Fatima Nasrallah
W. Syeda
Nian Wang
Jelle Veraart
Alard J. Roebroeck
Andrew F Bagdasarian
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
L. Soustelle
Christien Bowman
Benjamin C. Tendler … (see 38 more)
A. Hertanu
Ben Jeurissen
M. Verhoye
L. Frydman
Y. Looij
David C. Hike
Jeff F. Dunn
Karla L. Miller
Bennett A. Landman
N. Shemesh
Adam Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell’ Acqua
C. Pierpaoli
Ivana Drobnjak
Alexander Leemans
K. Harkins
Maxime Descoteaux
Duan Xu
Hao Huang
Mathieu D. Santin
Samuel C. Grant
Andre Obenaus
Gene S Kim
Dan Wu
D. Bihan
S. Blackband
Luisa Ciobanu
E. Fieremans
Ruiliang Bai
T. Leergaard
Jiangyang Zhang
T. Dyrby
G. A. Johnson
Julien Cohen-Adad
Matthew D. Budde
Ileana Ozana Jelescu
2025-02-26
Magnetic Resonance in Medicine (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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