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
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PhD - Université de Montréal
Independent visiting researcher
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Collaborating researcher - N/A
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PhD - Université de Montréal
Collaborating researcher - KAIST
Collaborating Alumni - Université de Montréal
PhD - Université de Montréal
Collaborating Alumni - Université de Montréal
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Independent visiting researcher
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PhD - Université de Montréal
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PhD - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
PhD - 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 Alumni - Université de Montréal
Postdoctorate - Université de Montréal
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Collaborating Alumni
Collaborating Alumni - Université de Montréal
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PhD - Université de Montréal
Collaborating Alumni - Université de Montréal
PhD - Université de Montréal
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PhD - Université de Montréal
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PhD - Université de Montréal
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Independent visiting researcher - Université de Montréal
PhD - Université de Montréal
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Collaborating researcher - Ying Wu Coll of Computing
Collaborating researcher - University of Waterloo
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Collaborating Alumni - Max-Planck-Institute for Intelligent Systems
Collaborating researcher - 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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Independent visiting researcher
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Postdoctorate - Université de Montréal
Collaborating Alumni - Université de Montréal
Collaborating Alumni - Université de Montréal
Postdoctorate
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Independent visiting researcher - Technical University of Munich
PhD - Université de Montréal
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Independent visiting researcher
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Collaborating Alumni - Université de Montréal
Postdoctorate - Université de Montréal
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PhD - Université de Montréal
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Collaborating researcher
Collaborating researcher - Université de Montréal
PhD - Université de Montréal
PhD - McGill University
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PhD - Université de Montréal
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Publications

Self-Evolving Curriculum for LLM Reasoning
Alex Pich'e
Nicolas Gontier
Ehsan Kamalloo
Latent Veracity Inference for Identifying Errors in Stepwise Reasoning
Jean-Pierre R. Falet
Oliver E. Richardson
Moksh J. Jain
Sungsoo Ahn
Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs); however, reasoning chains can cont… (see more)ain inaccurate statements that reduce performance and trustworthiness. To address this, we propose to augment each reasoning step in a CoT with a latent veracity (or correctness) variable. To efficiently explore this expanded space, we introduce Veracity Search (VS), a discrete search algorithm over veracity assignments. It performs otherwise intractable inference in the posterior distribution over latent veracity values by leveraging the LM's joint likelihood over veracity and the final answer as a proxy reward. This efficient inference-time verification method facilitates supervised fine-tuning of an Amortized Veracity Inference (AVI) machine by providing pseudo-labels for veracity. AVI generalizes VS, enabling accurate zero-shot veracity inference in novel contexts. Empirical results demonstrate that VS reliably identifies errors in logical (ProntoQA), mathematical (GSM8K), and commonsense (CommonsenseQA) reasoning benchmarks, with AVI achieving comparable zero-shot accuracy. Finally, we demonstrate the utility of latent veracity inference for providing feedback during self-correction and self-improvement.
Search-Based Correction of Reasoning Chains for Language Models
Jean-Pierre R. Falet
Oliver E. Richardson
Moksh J. Jain
Sungsoo Ahn
Can a Bayesian Oracle Prevent Harm from an Agent?
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.
Adaptive Cyclic Diffusion for Inference Scaling
Gyubin Lee
Truong Nhat Nguyen Bao
Jaesik Yoon
Dongwoo Lee
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N
Andrew Robert Williams
Phillip Wozny
Soham Rajesh Phade
Kai-Hendrik Cohrs
Sunil Srinivasa
Koen Ponse
Yang Zhang
Prateek Gupta
Stephan Zheng
Li Li
Erman Acar
Monte Carlo Tree Diffusion for System 2 Planning
Jaesik Yoon
Hyeonseo Cho
Doojin Baek
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
Any well-behaved generative model over a variable …
Rejecting Hallucinated State Targets during Planning
Search-Based Correction of Reasoning Chains for Language Models
Jean-Pierre R. Falet
Oliver E. Richardson
Moksh J. Jain
Sungsoo Ahn
Self-Evolving Curriculum for LLM Reasoning
Structure-Aligned Protein Language Model
Can Chen
David Heurtel-Depeiges
Robert M. Vernon
Christopher J. Langmead