Yoshua Bengio

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

Jamal Abou Haibeh

Collaborating Alumni - McGill University

Berkes Anaïs

Collaborating researcher - Cambridge University

Principal supervisor :

Rim Assouel

PhD - Université de Montréal

Stefan Bauer

Independent visiting researcher

Co-supervisor :

Guillaume Lajoie

Shahana Chatterjee

Collaborating researcher - N/A

Principal supervisor :

PhD - Université de Montréal

Sanghyeok Choi

Collaborating researcher - KAIST

PhD - Université de Montréal

Collaborating Alumni - Université de Montréal

Co-supervisor :

Loubna Benabbou

Desmond Elliott

Independent visiting researcher

Principal supervisor :

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Jean-Pierre Falet

PhD - Université de Montréal

PhD

PhD - Université de Montréal

Moksh Jain

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

Collaborating Alumni - Université de Montréal

Hyeonah Kim

Postdoctorate - Université de Montréal

Principal supervisor :

Alex Hernández-García

Tabitha Edith Lee

Postdoctorate - Université de Montréal

Principal supervisor :

Collaborating Alumni

Collaborating Alumni - Université de Montréal

Cristian Dragos Manta

PhD - Université de Montréal

Co-supervisor :

Dhanya Sridhar

Sarthak Mittal

PhD - Université de Montréal

Principal supervisor :

Independent visiting researcher - Université de Montréal

Padideh Nouri

PhD - Université de Montréal

Principal supervisor :

Ali Parviz

Collaborating researcher - Ying Wu Coll of Computing

Lena Podina

Collaborating researcher - University of Waterloo

Principal supervisor :

Nassim Rahaman

Collaborating Alumni - Max-Planck-Institute for Intelligent Systems

Amine RAZIG

Collaborating researcher - Université de Montréal

Co-supervisor :

Loubna Benabbou

Jarrid Rector-Brooks

PhD - Université de Montréal

Danyal REHMAN

Postdoctorate - Université de Montréal

Oli RICHARDSON

Postdoctorate - Université de Montréal

Camille Rochefort-Boulanger

PhD - Université de Montréal

Principal supervisor :

Julie Hussin

Dragos Secrieru

Collaborating Alumni - Université de Montréal

Divya Sharma

Postdoctorate

Co-supervisor :

Alex Hernández-García

Mélisande Astrid Crystal Teng

Vincent Taboga

Collaborating Alumni - Polytechnique Montréal

Co-supervisor :

PhD - Université de Montréal

Co-supervisor :

Hugo Larochelle

Ivan Titov

Collaborating researcher

Principal supervisor :

Siva Reddy

Alex Tong

Collaborating Alumni - Université de Montréal

Collaborating Alumni - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Principal supervisor :

Collaborating researcher

Collaborating researcher - Université de Montréal

Tianyu Zhang

PhD - Université de Montréal

PhD - McGill University

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

Aaron Courville

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Harry Zhao

Collaborating Alumni - McGill University

Principal supervisor :

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

Scaling in the Service of Reasoning & Model-Based ML

April 4, 2023

Yoshua Bengio

Edward J. Hu

A collaboration between Mila and Relation Therapeutics to discover novel synergistic combinations of drugs in vitro

March 23, 2022

Paul Bertin

Jake P. Taylor-King

Yoshua Bengio

March 15, 2022

Generative Flow Networks

Yoshua Bengio

Publications

In-Context Reinforcement Learning through Bayesian Fusion of Context and Value Prior

Anaïs Berkes

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)

Integrating Generative and Experimental Platforms for Biomolecular Design

Chenghao Liu

Jarrid Rector-Brooks

Soojung Yang

Sidney Lisanza

Jacob Gershon

Lauren Hong

Pranam Chatterjee

Biomolecular design, through artificial engineering of proteins, ligands, nucleic acids, and cells, holds immense promise in addressing pres… (see more)sing medical, industrial, and environmental challenges. While generative machine learning has shown significant potential in this area, a disconnect exists with experimental biology: many ML research efforts prioritize static benchmark performance, potentially sidelining impactful biological applications. This workshop seeks to bridge this gap by bringing computationalists and experimentalists together, catalyzing a deeper interdisciplinary discourse. Together, we will explore the strengths and challenges of generative ML in biology, experimental integration of generative ML, and biological problems ready for ML. To attract high-quality and diverse research, we partnered with Nature Biotechnology for a special collection, and we created dedicated tracks for in-silico ML research and hybrid ML-experimental biology research. Our lineup features emerging leaders as speakers and renowned scientists as panelists, encapsulating a spectrum from high-throughput experimentation and computational biology to generative ML. To catalyze new collaborations, we will host a seed-grant competition for pairs of experimentalists and computationalists proposing fresh joint projects. To connect dry and wet lab practice, a wet-lab challenge sponsored by Adaptyv Bio will empirically evaluate protein design models. With a diverse organizing team and backed by industry sponsors, we dedicate the workshop to pushing the boundaries of ML's role in biology. This will be the third edition of this workshop following the previous versions of it we organized at ICLR 2024 and 2025.

2025-12-31

Workshop Proposals @ International Conference on Learning Representations (published)

openreview.net

A Comedy of Estimators: On KL Regularization in RL Training of LLMs

Vedant Shah

Johan Obando-Ceron

Vineet Jain

Brian Bartoldson

Bhavya Kailkhura

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)

Hidden sampling biases inflate performance in gene regulatory network inference

Marco Stock

Florin Ratajczak

Paul Bertin

Eva Hoermanseder

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)

A Message from AI Research Leaders: Join Us in Supporting OpenReview

Andrew Y. Ng

Ruslan Salakhutdinov

Fernando Pereira

2025-12-17

OpenReview (unknown)

openreview.net

International AI Safety Report Second Key Update: Technical Safeguards and Risk Management

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)

Adsorption energies are necessary but not sufficient to identify good catalysts

Alexander Davis

Alexandre AGM Duval

Oleksandr Voznyy

Alex Hern'andez-Garcia

Félix Therrien

2025-12-04

ArXiv (preprint)

FALCON: Few-step Accurate Likelihoods for Continuous Flows

Artem Gazizov

2025-11-30

arXiv (published)

Sliding Window Recurrences for Sequence Models

Dragos Secrieru

Garyk Brixi

Taiji Suzuki

Michael Poli

Stefano Massaroli

Multi-hybrid architectures are poised to take over language modeling due to better quality and performance. We introduce a hierarchical deco… (see more)mposition framework for linear recurrences that allows us to develop algorithms aligned with GPU memory hierarchies, yielding Sliding Window Recurrences. We focus specifically on truncating recurrences to hardware-aligned windows which are naturally jagged, limiting costly inter-warp communication. Using SWR, we develop Phalanx layers that serve as drop-in replacements for windowed attention or linear recurrences. In 1B parameter multi-hybrid models, Phalanx achieves over 10-40% speedup across 4K to 32K context length over optimized Transformers while matching perplexity.

2025-11-30

arXiv (published)

Leveraging a Fully Differentiable Integrated Assessment Model for RL and Inference

Koen Ponse

Kai-Hendrik Cohrs

Phillip Wozny

Andrew Robert Williams

Tianyu Zhang

Erman Acar

Aske Plaat

Thomas M. Moerland

Pierre Gentine

Gustau Camps-Valls

2025-11-20

EurIPS.cc/2025/Workshop/DiffSys (published)

openreview.net

A HOT Dataset: 150,000 Buildings for HVAC Operations Transfer Research

Anaïs Berkes

Donna Vakalis

About 12% of global energy consumption is attributable to heating, ventilation, and air conditioning (HVAC) systems in buildings [11]. Machi… (see more)ne learning-based intelligent HVAC control offers significant energy efficiency potential, but progress is constrained by limited data for training and evaluating performance across different kinds of buildings. Existing datasets primarily target energy prediction rather than control applications, forcing studies to rely on limited building sets or single-variable perturbations that fail to capture real-world complexity. We present HOT (HVAC Operations Transfer), the first large-scale open-source dataset purpose-built for research into transfer learning in building control. HOT contains 159,744 unique building-weather combinations with systematic variations across envelope properties, occupancy patterns, and climate conditions spanning all 19 ASHRAE climate zones across 76 global locations. We formalise a comprehensive similarity-based framework with quantitative metrics for assessing transfer feasibility between source and target buildings across multiple context dimensions. Our key contributions: (1) a large-scale, open dataset and tooling enabling systematic, multi-variable transfer studies across 19 climate zones; (2) a quantitative similarity framework spanning geometry, thermal, climate, and function; and (3) zero-shot climate transfer experiments showing why realistic context variation matters for HVAC control.

2025-11-10

ACM Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (published)

A HOT Dataset: 150,000 Buildings for HVAC Operations Transfer Research

Anaïs Berkes

Donna Vakalis

2025-11-10

Proceedings of the 12th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (published)