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
Website
Google Scholar

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.

Publications

A Comparative Study of Molecular Dynamics Approaches for Simulating Ionic Conductivity in Solid Lithium Electrolytes
Dounia Shaaban Kabakibo
Félix Therrien
Yoshua Bengio
Michel Côté
Hongyu Guo
Homin Shin
Alex Hernandez-Garcia
Accurate prediction of ionic conductivity is critical for the design of highperformance solid-state electrolytes in next-generation batterie… (see more)s. We benchmark molecular dynamics (MD) approaches for computing ionic conductivity in 21 lithium solid electrolytes for which experimental ionic conductivity has been previously reported in the literature. Specifically, we compare simulations driven by density functional theory (DFT) and by universal machine-learning interatomic potentials (uMLIPs), namely a MACE foundation model. Our results suggest comparable performance between DFT and MACE, with MACE requiring only a fraction of the computational cost. The framework developed here is designed to enable systematic comparisons with additional uMLIPs and fine-tuned models in future work.
2026-03-01
AI4Mat @ International Conference on Learning Representations (poster)
openreview.net
openreview.net
Navigating ternary doping in Li-ion cathodes with closed-loop multi-objective Bayesian optimization
Nooshin Zeinali Galabi
Cheng-Hao Liu
Moksh Jain
Marc Kamel
Shipeng Jia
Yoshua Bengio
Eric McCalla
To further improve secondary battery materials, we are increasingly exploring highly complex composition spaces in attempts to optimize mult… (see more)iple properties simultaneously. While our past work has done this in systematic manners using high-throughput experimentation, the exponential increase in the search space with triple doping makes grid search prohibitively expensive. Here, we demonstrate a closed-loop, multi-objective machine learning approach to guide the high-throughput workflow to efficiently navigate a space with approximately 14 million unique combinations. The test system is LiCoPO4 which we have previously explored using systematic codoping that was effective in optimizing one property only: energy density. To learn multiple electrochemical metrics, we first pretrain a set transformer on the public Materials Project database as a feature extractor, then attach a multi-task Gaussian process head and finetune the entire model on our high-throughput data. Through 3 rounds of active learning, we demonstrate that with a very small number of samples (as few as 125 random compositions and 63 predicted) we are able to simultaneously optimize four key electrochemical properties. Relative to the undoped system, the best composition raises our composite figure of merit by up to five times. This establishes an end-to-end workflow for accelerated battery materials design to be used in the rapidly growing field of autonomous materials discovery.
2026-02-11
Advances in Materials (published)
doi.org
Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
Hyeonah Kim
Minsu Kim
Celine ROGET
D. Biton
Louis Vaillancourt
Yves V. Brun
Yoshua Bengio
Alex Hernandez-Garcia
2026-02-03
arXiv (preprint)
doi.org
arxiv.org
Divergent creativity in humans and large language models
Antoine Bellemare-Pepin
François Lespinasse
Philipp Thölke
Yann Harel
Kory Mathewson
Jay A. Olson
Yoshua Bengio
Karim Jerbi
Psychology Department
U. Montr'eal
Montreal
Qc
Canada
Music department
C. University
Sociology
Anthropology department
Mila
Departmentof Psychology
University of Toronto Mississauga … (see 5 more)
Mississauga
On
Department of Computer Science
Operations Research
Unique Center
The recent surge of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin to human capabilitie… (see more)s. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLMs’ semantic diversity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in computational creativity to analyze semantic divergence in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. These divergence-based measures index associative thinking—the ability to access and combine remote concepts in semantic space—an established facet of creative cognition. We benchmark performance on the Divergent Association Task (DAT) and across multiple creative-writing tasks (haiku, story synopses, and flash fiction), using identical, objective scoring. We found evidence that LLMs can surpass average human performance on the DAT, and approach human creative writing abilities, yet they remain below the mean creativity scores observed among the more creative segment of human participants. Notably, even the top performing LLMs are still largely surpassed by the aggregated top half of human participants, underscoring a ceiling that current LLMs still fail to surpass. We also systematically varied linguistic strategy prompts and temperature, observing reliable gains in semantic divergence for several models. Our human-machine benchmarking framework addresses the polemic surrounding the imminent replacement of human creative labor by AI, disentangling the quality of the respective creative linguistic outputs using established objective measures. While prompting deeper exploration of the distinctive elements of human inventive thought compared to those of AI systems, we lay out a series of techniques to improve their outputs with respect to semantic diversity, such as prompt design and hyper-parameter tuning.
2026-01-20
Scientific Reports (published)
doi.org
arxiv.org
Discrete Feynman-Kac Correctors
Mohsin Hasan
Viktor Ohanesian
Artem Gazizov
Yoshua Bengio
Alán Aspuru-Guzik
Roberto Bondesan
Marta Skreta
Kirill Neklyudov
Discrete diffusion models have recently emerged as a promising alternative to the autoregressive approach for generating discrete sequences.… (see more) Sample generation via gradual denoising or demasking processes allows them to capture hierarchical non-sequential interdependencies in the data. These custom processes, however, do not assume a flexible control over the distribution of generated samples. We propose Discrete Feynman-Kac Correctors, a framework that allows for controlling the generated distribution of discrete masked diffusion models at inference time. We derive Sequential Monte Carlo (SMC) algorithms that, given a trained discrete diffusion model, control the temperature of the sampled distribution (i.e. perform annealing), sample from the product of marginals of several diffusion processes (e.g. differently conditioned processes), and sample from the product of the marginal with an external reward function, producing likely samples from the target distribution that also have high reward. Notably, our framework does not require any training of additional models or fine-tuning of the original model. We illustrate the utility of our framework in several applications including: efficient sampling from the annealed Boltzmann distribution of the Ising model, improving the performance of language models for code generation and amortized learning, as well as reward-tilted protein sequence generation.
2026-01-14
arXiv (preprint)
doi.org
arxiv.org
In-Context Reinforcement Learning through Bayesian Fusion of Context and Value Prior
Anaïs Berkes
Vincent Taboga
Donna Vakalis
David Rolnick
Yoshua Bengio
In-context reinforcement learning (ICRL) promises fast adaptation to unseen environments without parameter updates, but current methods eith… (see more)er cannot improve beyond the training distribution or require near-optimal data, limiting practical adoption. We introduce SPICE, a Bayesian ICRL method that learns a prior over Q-values via deep ensemble and updates this prior at test-time using in-context information through Bayesian updates. To recover from poor priors resulting from training on sub-optimal data, our online inference follows an Upper-Confidence Bound rule that favours exploration and adaptation. We prove that SPICE achieves regret-optimal behaviour in both stochastic bandits and finite-horizon MDPs, even when pretrained only on suboptimal trajectories. We validate these findings empirically across bandit and control benchmarks. SPICE achieves near-optimal decisions on unseen tasks, substantially reduces regret compared to prior ICRL and meta-RL approaches while rapidly adapting to unseen tasks and remaining robust under distribution shift.
2025-12-31
arXiv (preprint)
doi.org
arxiv.org
A Comedy of Estimators: On KL Regularization in RL Training of LLMs
Vedant Shah
Johan Obando-Ceron
Vineet Jain
Brian Bartoldson
Bhavya Kailkhura
Sarthak Mittal
Glen Berseth
Pablo Samuel Castro
Yoshua Bengio
Nikolay Malkin
Moksh Jain
Siddarth Venkatraman
Aaron Courville
The reasoning performance of large language models (LLMs) can be substantially improved by training them with reinforcement learning (RL). T… (see more)he RL objective for LLM training involves a regularization term, which is the reverse Kullback-Leibler (KL) divergence between the trained policy and the reference policy. Since computing the KL divergence exactly is intractable, various estimators are used in practice to estimate it from on-policy samples. Despite its wide adoption, including in several open-source libraries, there is no systematic study analyzing the numerous ways of incorporating KL estimators in the objective and their effect on the downstream performance of RL-trained models. Recent works show that prevailing practices for incorporating KL regularization do not provide correct gradients for stated objectives, creating a discrepancy between the objective and its implementation. In this paper, we further analyze these practices and study the gradients of several estimators configurations, revealing how design choices shape gradient bias. We substantiate these findings with empirical observations by RL fine-tuning \texttt{Qwen2.5-7B}, \texttt{Llama-3.1-8B-Instruct} and \texttt{Qwen3-4B-Instruct-2507} with different configurations and evaluating their performance on both in- and out-of-distribution tasks. Through our analysis, we observe that, in on-policy settings: (1) estimator configurations with biased gradients can result in training instabilities; and (2) using estimator configurations resulting in unbiased gradients leads to better performance on in-domain as well as out-of-domain tasks. We also investigate the performance resulting from different KL configurations in off-policy settings and observe that KL regularization can help stabilize off-policy RL training resulting from asynchronous setups.
2025-12-25
ArXiv (preprint)
doi.org
arxiv.org
Hidden sampling biases inflate performance in gene regulatory network inference
Marco Stock
Florin Ratajczak
Paul Bertin
Eva Hoermanseder
Yoshua Bengio
Jason Hartford
Pascal Falter-Braun
Matthias Heinig
Alexander Tong
Antonio Scialdone
Accurate reconstruction of gene regulatory networks (GRNs) from single-cell transcriptomic data remains a major methodological challenge. Re… (see more)cent machine learning approaches, particularly graph neural networks and graph autoencoders, have reported improved performance, yet these gains do not consistently translate to realistic biological settings. Here, we show that a key reason for that is the way negative regulatory interactions are sampled for supervised training and evaluation. We find that widely used sampling strategies introduce node-degree biases that allow models to exploit trivial graph-structural cues rather than biological signals. Across multiple benchmarks, simple degree-based heuristics match or exceed state-of-the-art graph neural network models under these biased evaluation protocols. We further introduce a degree-aware sampling approach that eliminates these artifacts and provides more reliable assessments of GRN inference methods. Our results call for standardized, bias-aware benchmarking practices to ensure meaningful progress in supervised GRN inference from single-cell RNA-seq data.
2025-12-22
bioRxiv (preprint)
doi.org
A Message from AI Research Leaders: Join Us in Supporting OpenReview
Joelle Pineau
Doina Precup
Kyunghyun Cho
Andrew Y. Ng
Yoshua Bengio
Ruslan Salakhutdinov
Fernando Pereira
2025-12-17
OpenReview (unknown)
openreview.net
openreview.net
International AI Safety Report Second Key Update: Technical Safeguards and Risk Management
Yoshua Bengio
Stephen Clare
Carina Prunkl
Maksym Andriushchenko
BEN BUCKNALL
Philip Fox
Nestor Maslej
Conor McGlynn
Malcolm Murray
Shalaleh Rismani
Stephen Casper
Jessica Newman
Daniel Privitera
Sören Mindermann
Daron Acemoglu
Thomas G. Dietterich
Fredrik Heintz
Geoffrey Hinton
Nick Jennings
Susan Leavy … (see 17 more)
Teresa Ludermir
Vidushi Marda
Helen Margetts
John McDermid
Jane Munga
Arvind Narayanan
Alondra Nelson
Clara Neppel
Sarvapali D. (Gopal) Ramchurn
Stuart Russell
Marietje Schaake
Bernhard Schölkopf
Alvaro Soto
Lee Tiedrich
Gael Varoquaux
Andrew Yao
Ya-Qin Zhang
This is the Second Key Update to the 2025 International AI Safety Report. The First Key Update (1) discussed developments in the capabilitie… (see more)s of general-purpose AI models and systems and associated risks. This Key Update covers how various actors, including researchers, companies, and governments, are approaching risk management and technical mitigations for AI. The past year has seen important developments in AI risk management, including better techniques for training safer models and monitoring their outputs. While this represents tangible progress, significant gaps remain. It is often uncertain how effective current measures are at preventing harms, and effectiveness varies across time and applications. There are many opportunities to further strengthen existing safeguard techniques and to develop new ones. This Key Update provides a concise overview of critical developments in risk management practices and technical risk mitigation since the publication of the 2025 AI Safety Report in January. It highlights where progress is being made and where gaps remain. Above all, it aims to support policymakers, researchers, and the public in navigating a rapidly changing environment, helping them to make informed and timely decisions about the governance of general-purpose AI. Professor Yoshua BengioUniversité de Montréal / LawZero /Mila – Quebec AI Institute & Chair
2025-12-06
SuperIntelligence - Robotics - Safety & Alignment (published)
doi.org
Adsorption energies are necessary but not sufficient to identify good catalysts
Shahana Chatterjee
Alexander Davis
Lena Podina
Divya Sharma
Yoshua Bengio
Alexandre AGM Duval
Oleksandr Voznyy
Alex Hern'andez-Garcia
David Rolnick
Félix Therrien
2025-12-04
ArXiv (preprint)
arxiv.org
arxiv.org
FALCON: Few-step Accurate Likelihoods for Continuous Flows
Danyal REHMAN
Tara Akhound-Sadegh
Artem Gazizov
Yoshua Bengio
Alexander Tong
2025-11-30
arXiv (published)
doi.org
arxiv.org