Portrait of Yoshua Bengio

Yoshua Bengio

Core Academic Member
Canada CIFAR AI Chair
Full Professor, Université de Montréal, Department of Computer Science and Operations Research Department
Founder and Scientific Advisor, Leadership Team
Research Topics
Causality
Computational Neuroscience
Deep Learning
Generative Models
Graph Neural Networks
Machine Learning Theory
Medical Machine Learning
Molecular Modeling
Natural Language Processing
Probabilistic Models
Reasoning
Recurrent Neural Networks
Reinforcement Learning
Representation Learning

Biography

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Yoshua Bengio is recognized worldwide as a leading expert in AI. He is most known for his pioneering work in deep learning, which earned him the 2018 A.M. Turing Award, “the Nobel Prize of computing,” with Geoffrey Hinton and Yann LeCun.

Bengio is a full professor at Université de Montréal, and the founder and scientific advisor of Mila – Quebec Artificial Intelligence Institute. He is also a senior fellow at CIFAR and co-directs its Learning in Machines & Brains program, serves as special advisor and founding scientific director of IVADO, and holds a Canada CIFAR AI Chair.

In 2019, Bengio was awarded the prestigious Killam Prize and in 2022, he was the most cited computer scientist in the world by h-index. He is a Fellow of the Royal Society of London, Fellow of the Royal Society of Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. In 2023, he was appointed to the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology.

Concerned about the social impact of AI, Bengio helped draft the Montréal Declaration for the Responsible Development of Artificial Intelligence and continues to raise awareness about the importance of mitigating the potentially catastrophic risks associated with future AI systems.

Current Students

Collaborating Alumni - McGill University
Collaborating Alumni - Université de Montréal
Collaborating researcher - Cambridge University
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PhD - Université de Montréal
Independent visiting researcher - KAIST
Independent visiting researcher
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PhD - Université de Montréal
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PhD - Université de Montréal
Collaborating researcher - KAIST
PhD - Université de Montréal
PhD - Université de Montréal
Research Intern - Université de Montréal
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PhD - Université de Montréal
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PhD - Université de Montréal
PhD - Université de Montréal
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PhD - Université de Montréal
Research Intern - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
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Collaborating Alumni - Université de Montréal
Postdoctorate - Université de Montréal
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Collaborating researcher - Université de Montréal
Collaborating Alumni - Université de Montréal
Postdoctorate - Université de Montréal
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Collaborating Alumni - Université de Montréal
Collaborating Alumni
Collaborating Alumni - Université de Montréal
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PhD - Université de Montréal
Collaborating Alumni - Université de Montréal
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PhD - Université de Montréal
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Collaborating researcher - Ying Wu Coll of Computing
PhD - University of Waterloo
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Collaborating Alumni - Max-Planck-Institute for Intelligent Systems
Research Intern - Université de Montréal
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PhD - Université de Montréal
Postdoctorate - Université de Montréal
Independent visiting researcher - Université de Montréal
Postdoctorate - Université de Montréal
PhD - Université de Montréal
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Collaborating Alumni - Université de Montréal
Postdoctorate - Université de Montréal
Master's Research - Université de Montréal
Collaborating Alumni - Université de Montréal
Master's Research - Université de Montréal
Postdoctorate
Independent visiting researcher - Technical University of Munich
PhD - Université de Montréal
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Postdoctorate - Université de Montréal
Postdoctorate - Université de Montréal
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PhD - Université de Montréal
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Collaborating researcher - Université de Montréal
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Collaborating researcher - KAIST
PhD - McGill University
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Publications

RL, but don't do anything I wouldn't do
Michael K. Cohen
Marcus Hutter
Stuart Russell
In reinforcement learning (RL), if the agent's reward differs from the designers' true utility, even only rarely, the state distribution res… (see more)ulting from the agent's policy can be very bad, in theory and in practice. When RL policies would devolve into undesired behavior, a common countermeasure is KL regularization to a trusted policy ("Don't do anything I wouldn't do"). All current cutting-edge language models are RL agents that are KL-regularized to a "base policy" that is purely predictive. Unfortunately, we demonstrate that when this base policy is a Bayesian predictive model of a trusted policy, the KL constraint is no longer reliable for controlling the behavior of an advanced RL agent. We demonstrate this theoretically using algorithmic information theory, and while systems today are too weak to exhibit this theorized failure precisely, we RL-finetune a language model and find evidence that our formal results are plausibly relevant in practice. We also propose a theoretical alternative that avoids this problem by replacing the "Don't do anything I wouldn't do" principle with "Don't do anything I mightn't do".
Can a Bayesian Oracle Prevent Harm from an Agent?
Michael K. Cohen
Nikolay Malkin
Matt MacDermott
Damiano Fornasiere
Pietro Greiner
Younesse Kaddar
Is there a way to design powerful AI systems based on machine learning methods that would satisfy probabilistic safety guarantees? With the … (see more)long-term goal of obtaining a probabilistic guarantee that would apply in every context, we consider estimating a context-dependent bound on the probability of violating a given safety specification. Such a risk evaluation would need to be performed at run-time to provide a guardrail against dangerous actions of an AI. Noting that different plausible hypotheses about the world could produce very different outcomes, and because we do not know which one is right, we derive bounds on the safety violation probability predicted under the true but unknown hypothesis. Such bounds could be used to reject potentially dangerous actions. Our main results involve searching for cautious but plausible hypotheses, obtained by a maximization that involves Bayesian posteriors over hypotheses. We consider two forms of this result, in the iid case and in the non-iid case, and conclude with open problems towards turning such theoretical results into practical AI guardrails.
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N
Tianyu Zhang
Andrew Robert Williams
Phillip Wozny
Kai-Hendrik Cohrs
Koen Ponse
Marco Jiralerspong
Soham Rajesh Phade
Sunil Srinivasa
Lu Liu
Yang Zhang
Prateek Gupta
Erman Acar
Stephan Zheng
Monte Carlo Tree Diffusion for System 2 Planning
Jaesik Yoon
Hyeonseo Cho
Doojin Baek
Sungjin Ahn
Diffusion models have recently emerged as a powerful tool for planning. However, unlike Monte Carlo Tree Search (MCTS)-whose performance nat… (see more)urally improves with additional test-time computation (TTC), standard diffusion-based planners offer only limited avenues for TTC scalability. In this paper, we introduce Monte Carlo Tree Diffusion (MCTD), a novel framework that integrates the generative strength of diffusion models with the adaptive search capabilities of MCTS. Our method reconceptualizes denoising as a tree-structured process, allowing partially denoised plans to be iteratively evaluated, pruned, and refined. By selectively expanding promising trajectories while retaining the flexibility to revisit and improve suboptimal branches, MCTD achieves the benefits of MCTS such as controlling exploration-exploitation trade-offs within the diffusion framework. Empirical results on challenging long-horizon tasks show that MCTD outperforms diffusion baselines, yielding higher-quality solutions as TTC increases.
Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models
Siddarth Venkatraman
Mohsin Hasan
Minsu Kim
Luca Scimeca
Marcin Sendera
Nikolay Malkin
Any well-behaved generative model over a variable …
Rejecting Hallucinated State Targets during Planning
Harry Zhao
Mingde Zhao
Tristan Sylvain
Romain Laroche
Open Problems in Technical AI Governance
Anka Reuel
Benjamin Bucknall
Stephen Casper
Timothy Fist
Lisa Soder
Onni Aarne
Lewis Hammond
Lujain Ibrahim
Alan Chan
Peter Wills
Markus Anderljung
Ben Garfinkel
Lennart Heim
Andrew Trask
Gabriel Mukobi
Rylan Schaeffer
Mauricio Baker
Sara Hooker
Irene Solaiman
Sasha Luccioni … (see 14 more)
Alexandra Luccioni
Nitarshan Rajkumar
Nicolas Moës
Jeffrey Ladish
David Bau
Paul Bricman
Neel Guha
Jessica Newman
Tobin South
Alex Pentland
Sanmi Koyejo
Mykel Kochenderfer
Robert Trager
AI progress is creating a growing range of risks and opportunities, but it is often unclear how they should be navigated. In many cases, the… (see more) barriers and uncertainties faced are at least partly technical. Technical AI governance, referring to technical analysis and tools for supporting the effective governance of AI, seeks to address such challenges. It can help to (a) identify areas where intervention is needed, (b) identify and assess the efficacy of potential governance actions, and (c) enhance governance options by designing mechanisms for enforcement, incentivization, or compliance. In this paper, we explain what technical AI governance is, why it is important, and present a taxonomy and incomplete catalog of its open problems. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
Mélisande Teng
Arthur Ouaknine
Etienne Lalibert'e
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
Mélisande Teng
Arthur Ouaknine
Etienne Lalibert'e
Extendable Long-Horizon Planning via Hierarchical Multiscale Diffusion
Chang Chen
Hany Hamed
Doojin Baek
Taegu Kang
Sungjin Ahn
Extendable Long-Horizon Planning via Hierarchical Multiscale Diffusion
Chang Chen
Hany Hamed
Doojin Baek
Taegu Kang
Sungjin Ahn
A scalable gene network model of regulatory dynamics in single cells
Paul Bertin
Joseph D Viviano
Alejandro Tejada-Lapuerta
Weixu Wang
Stefan Bauer
Fabian J. Theis