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
Scientific Director, Leadership Team
Observer, Board of Directors, Mila

Biography

*For media requests, please write to medias@mila.quebec.

For more information please contact Julie Mongeau, executive assistant at julie.mongeau@mila.quebec.

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 director 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 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

Research Intern - Université de Montréal
PhD - Université de Montréal
Research Intern - Université du Québec à Rimouski
Professional Master's - Université de Montréal
Independent visiting researcher
Co-supervisor :
Independent visiting researcher - UQAR
PhD - Université de Montréal
Independent visiting researcher - MIT
PhD - Université de Montréal
Postdoctorate - Université de Montréal
Co-supervisor :
Professional Master's - Université de Montréal
Professional Master's - Université de Montréal
Collaborating Alumni - Université de Montréal
Collaborating researcher - Université Paris-Saclay
Principal supervisor :
PhD - Université de Montréal
PhD - Massachusetts Institute of Technology
PhD - Université de Montréal
PhD - Université de Montréal
Professional Master's - Université de Montréal
Professional Master's - Université de Montréal
Professional Master's - Université de Montréal
Collaborating researcher
Postdoctorate - Université de Montréal
Co-supervisor :
Independent visiting researcher - Technical University Munich (TUM)
PhD - Université de Montréal
Research Intern - Université de Montréal
Master's Research - Université de Montréal
Co-supervisor :
Research Intern - Université de Montréal
Collaborating researcher - Université de Montréal
PhD - Université de Montréal
Postdoctorate - Université de Montréal
PhD - Université de Montréal
Collaborating Alumni
Research Intern - Université de Montréal
Professional Master's - Université de Montréal
PhD - Université de Montréal
Principal supervisor :
Research Intern - McGill University
Research Intern - Imperial College London
PhD - Université de Montréal
Research Intern - Université de Montréal
Collaborating Alumni - Université de Montréal
DESS - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
Postdoctorate - Université de Montréal
Collaborating researcher - Université de Montréal
PhD - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
Principal supervisor :
Professional Master's - Université de Montréal
Independent visiting researcher - Université de Montréal
Independent visiting researcher - Hong Kong University of Science and Technology (HKUST)
Collaborating researcher - Ying Wu Coll of Computing
Professional Master's - Université de Montréal
Undergraduate - Université de Montréal
PhD - Max-Planck-Institute for Intelligent Systems
Professional Master's - Université de Montréal
Independent visiting researcher - Université de Montréal
Independent visiting researcher - Université de Montréal
PhD - Université de Montréal
Principal supervisor :
Collaborating researcher
Principal supervisor :
Postdoctorate - Université de Montréal
Master's Research - Université de Montréal
Research Intern - Université de Montréal
Master's Research - Université de Montréal
Professional Master's - Université de Montréal
Independent visiting researcher - Technical University of Munich
PhD - École Polytechnique Montréal Fédérale de Lausanne
PhD - Université de Montréal
Co-supervisor :
Collaborating researcher
Principal supervisor :
Postdoctorate - Université de Montréal
Collaborating researcher - Valence
Principal supervisor :
Postdoctorate - Université de Montréal
Co-supervisor :
Collaborating researcher - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)
Principal supervisor :
PhD - Université de Montréal
Professional Master's - Université de Montréal
Collaborating Alumni - Université de Montréal
Research Intern - Université de Montréal
PhD - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
Principal supervisor :
PhD - McGill University
Principal supervisor :
PhD - McGill University
Principal supervisor :

Publications

Neural Causal Structure Discovery from Interventions
Nan Rosemary Ke
Olexa Bilaniuk
Anirudh Goyal
Stefan Bauer
Bernhard Schölkopf
Michael Curtis Mozer
Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (see more) However, there are theoretical limitations on the identifiability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository.
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Patrick Mark Butlin
R. Long
Eric Elmoznino
Jonathan C. P. Birch
Axel Constant
George Deane
S. Fleming
C. Frith
Xuanxiu Ji
Ryota Kanai
C. Klein
Grace W. Lindsay
Matthias Michel
Liad Mudrik
Megan A. K. Peters
Eric Schwitzgebel
Jonathan Simon
Rufin Vanrullen
Scientific discovery in the age of artificial intelligence
Hanchen Wang
Tianfan Fu
Yuanqi Du
Wenhao Gao
Kexin Huang
Ziming Liu
Payal Chandak
Shengchao Liu
Peter Van Katwyk
Andreea Deac
Animashree Anandkumar
K. Bergen
Carla P. Gomes
Shirley Ho
Pushmeet Kohli
Joan Lasenby
Jure Leskovec
Tie-Yan Liu
A. Manrai
Debora Susan Marks … (see 10 more)
Bharath Ramsundar
Le Song
Jimeng Sun
Petar Veličković
Max Welling
Linfeng Zhang
Connor Wilson. Coley
Marinka Žitnik
What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?
Oussama Boussif
Ghait Boukachab
Dan Assouline
Stefano Massaroli
Tianle Yuan
Loubna Benabbou
The global integration of solar power into the electrical grid could have a crucial impact on climate change mitigation, yet poses a challen… (see more)ge due to solar irradiance variability. We present a deep learning architecture which uses spatio-temporal context from satellite data for highly accurate day-ahead time-series forecasting, in particular Global Horizontal Irradiance (GHI). We provide a multi-quantile variant which outputs a prediction interval for each time-step, serving as a measure of forecasting uncertainty. In addition, we suggest a testing scheme that separates easy and difficult scenarios, which appears useful to evaluate model performance in varying cloud conditions. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective use of solar power and the resulting reduction of CO
What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?
Oussama Boussif
Ghait Boukachab
Dan Assouline
Stefano Massaroli
Tianle Yuan
Loubna Benabbou
AI For Global Climate Cooperation 2023 Competition Proceedings
Prateek Arun Gupta
Lu Li
Soham R. Phade
Sunil Srinivasa
andrew williams
Tianyu Zhang
Yang Zhang
Stephan Tao Zheng
The international community must collaborate to mitigate climate change and sustain economic growth. However, collaboration is hard to achie… (see more)ve, partly because no global authority can ensure compliance with international climate agreements. Combining AI with climate-economic simulations offers a promising solution to design international frameworks, including negotiation protocols and climate agreements, that promote and incentivize collaboration. In addition, these frameworks should also have policy goals fulfillment, and sustained commitment, taking into account climate-economic dynamics and strategic behaviors. These challenges require an interdisciplinary approach across machine learning, economics, climate science, law, policy, ethics, and other fields. Towards this objective, we organized AI for Global Climate Cooperation, a Mila competition in which teams submitted proposals and analyses of international frameworks, based on (modifications of) RICE-N, an AI-driven integrated assessment model (IAM). In particular, RICE-N supports modeling regional decision-making using AI agents. Furthermore, the IAM then models the climate-economic impact of those decisions into the future. Whereas the first track focused only on performance metrics, the proposals submitted to the second track were evaluated both quantitatively and qualitatively. The quantitative evaluation focused on a combination of (i) the degree of mitigation of global temperature rise and (ii) the increase in economic productivity. On the other hand, an interdisciplinary panel of human experts in law, policy, sociology, economics and environmental science, evaluated the solutions qualitatively. In particular, the panel considered the effectiveness, simplicity, feasibility, ethics, and notions of climate justice of the protocols. In the third track, the participants were asked to critique and improve RICE-N.
AI For Global Climate Cooperation 2023 Competition Proceedings
Prateek Arun Gupta
Lu Li
Soham R. Phade
Sunil Srinivasa
andrew williams
Tianyu Zhang
Yangtian Zhang
Stephan Tao Zheng
The international community must collaborate to mitigate climate change and sustain economic growth. However, collaboration is hard to achie… (see more)ve, partly because no global authority can ensure compliance with international climate agreements. Combining AI with climate-economic simulations offers a promising solution to design international frameworks, including negotiation protocols and climate agreements, that promote and incentivize collaboration. In addition, these frameworks should also have policy goals fulfillment, and sustained commitment, taking into account climate-economic dynamics and strategic behaviors. These challenges require an interdisciplinary approach across machine learning, economics, climate science, law, policy, ethics, and other fields. Towards this objective, we organized AI for Global Climate Cooperation, a Mila competition in which teams submitted proposals and analyses of international frameworks, based on (modifications of) RICE-N, an AI-driven integrated assessment model (IAM). In particular, RICE-N supports modeling regional decision-making using AI agents. Furthermore, the IAM then models the climate-economic impact of those decisions into the future. Whereas the first track focused only on performance metrics, the proposals submitted to the second track were evaluated both quantitatively and qualitatively. The quantitative evaluation focused on a combination of (i) the degree of mitigation of global temperature rise and (ii) the increase in economic productivity. On the other hand, an interdisciplinary panel of human experts in law, policy, sociology, economics and environmental science, evaluated the solutions qualitatively. In particular, the panel considered the effectiveness, simplicity, feasibility, ethics, and notions of climate justice of the protocols. In the third track, the participants were asked to critique and improve RICE-N.
International Institutions for Advanced AI
Lewis Ho
Joslyn N. Barnhart
Robert Frederic Trager
Miles Brundage
Allison Sovey Carnegie
Rumman Chowdhury
Allan Dafoe
Gillian K. Hadfield
Margaret Levi
D. Snidal
Generative Flow Networks: a Markov Chain Perspective
Tristan Deleu
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Ling Pan
Nikolay Malkin
Dinghuai Zhang
Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov chain methods (as they sample from a distribution specified by an en… (see more)ergy function), reinforcement learning (as they learn a policy to sample composed objects through a sequence of steps), generative models (as they learn to represent and sample from a distribution) and amortized variational methods (as they can be used to learn to approximate and sample from an otherwise intractable posterior, given a prior and a likelihood). They are trained to generate an object
Discrete Key-Value Bottleneck
Frederik Träuble
Anirudh Goyal
Nasim Rahaman
Michael Curtis Mozer
Kenji Kawaguchi
Bernhard Schölkopf
Equivariance with Learned Canonicalization Functions
Sékou-Oumar Kaba
Arnab Kumar Mondal
Yan Zhang