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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
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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

Sparse Decomposition of Graph Neural Networks
Yaochen Hu
Mai Zeng
Ge Zhang
Pavel Rumiantsev
Liheng Ma
Yingxue Zhang
Mark Coates
2025-03-17
TMLR (accepted)
doi.org
openreview.net
The BrowserGym Ecosystem for Web Agent Research
Thibault Le Sellier de Chezelles
Maxime Gasse
Alexandre Lacoste
Alexandre Drouin
Massimo Caccia
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
Quentin Cappart
Graham Neubig
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
NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wild
Shikhar Murty
Hao Zhu
Dzmitry Bahdanau
Christopher D Manning
We introduce NNetNav, a method for unsupervised interaction with websites that generates synthetic demonstrations for training browser agent… (see more)s. Given any website, NNetNav produces these demonstrations by retroactively labeling action sequences from an exploration policy. Most work on training browser agents has relied on expensive human supervision, and the limited prior work on such interaction-based techniques has failed to provide effective search through the exponentially large space of exploration. In contrast, NNetNav exploits the hierarchical structure of language instructions to make this search more tractable: Complex instructions are typically decomposable into simpler sub-tasks, allowing NNetNav to automatically prune interaction episodes when an intermediate trajectory cannot be annotated with a meaningful sub-task. \texttt{LLama-3.1-8b} finetuned on 10k NNetNav self-generated demonstrations obtains over 16\% success rate on WebArena, and 35\% on WebVoyager, an improvement of 15pts and 31pts respectively over zero-shot \texttt{LLama-3.1-8b}, outperforming zero-shot GPT-4 and reaching the state-of-the-art among unsupervised methods, for both benchmarks.
2025-03-07
ICLR.cc/2025/Workshop/SSI-FM (poster)
openreview.net
openreview.net
NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wild
Shikhar Murty
Dzmitry Bahdanau
Hao Zhu
Christopher D Manning
2025-03-07
ICLR.cc/2025/Workshop/SSI-FM (poster)
openreview.net
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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