Portrait de Yoshua Bengio

Yoshua Bengio

Membre académique principal
Chaire en IA Canada-CIFAR
Professeur titulaire, Université de Montréal, Département d'informatique et de recherche opérationnelle
Directeur scientifique, Équipe de direction
Observateur, Conseil d'administration, Mila

Biographie

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Pour plus d’information, contactez Julie Mongeau, adjointe de direction à julie.mongeau@mila.quebec.

Reconnu comme une sommité mondiale en intelligence artificielle, Yoshua Bengio s’est surtout distingué par son rôle de pionnier en apprentissage profond, ce qui lui a valu le prix A. M. Turing 2018, le « prix Nobel de l’informatique », avec Geoffrey Hinton et Yann LeCun. Il est professeur titulaire à l’Université de Montréal, fondateur et directeur scientifique de Mila – Institut québécois d’intelligence artificielle, et codirige en tant que senior fellow le programme Apprentissage automatique, apprentissage biologique de l'Institut canadien de recherches avancées (CIFAR). Il occupe également la fonction de directeur scientifique d’IVADO.

En 2018, il a été l’informaticien qui a recueilli le plus grand nombre de nouvelles citations au monde. En 2019, il s’est vu décerner le prestigieux prix Killam. Depuis 2022, il détient le plus grand facteur d’impact (h-index) en informatique à l’échelle mondiale. Il est fellow de la Royal Society de Londres et de la Société royale du Canada, et officier de l’Ordre du Canada.

Soucieux des répercussions sociales de l’IA et de l’objectif que l’IA bénéficie à tous, il a contribué activement à la Déclaration de Montréal pour un développement responsable de l’intelligence artificielle.

Étudiants actuels

Stagiaire de recherche - Université du Québec à Rimouski
Maîtrise professionnelle - UdeM
Visiteur de recherche indépendant
Co-superviseur⋅e :
Visiteur de recherche indépendant - UQAR
Stagiaire de recherche - UQAR
Visiteur de recherche indépendant - MIT
Postdoctorat - UdeM
Co-superviseur⋅e :
Maîtrise professionnelle - UdeM
Collaborateur·rice de recherche - Université Paris-Saclay
Superviseur⋅e principal⋅e :
Doctorat - Massachusetts Institute of Technology
Doctorat - Barcelona University
Maîtrise professionnelle - UdeM
Maîtrise professionnelle - UdeM
Collaborateur·rice de recherche
Visiteur de recherche indépendant - Technical University Munich (TUM)
Collaborateur·rice de recherche - UdeM
Collaborateur·rice alumni
Maîtrise professionnelle - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Stagiaire de recherche - Imperial College London
Stagiaire de recherche - UdeM
Collaborateur·rice de recherche - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Maîtrise professionnelle - UdeM
Visiteur de recherche indépendant - UdeM
Visiteur de recherche indépendant - Hong Kong University of Science and Technology (HKUST)
Collaborateur·rice de recherche - Ying Wu Coll of Computing
Maîtrise professionnelle - UdeM
Doctorat - Max-Planck-Institute for Intelligent Systems
Maîtrise professionnelle - UdeM
Visiteur de recherche indépendant - UdeM
Visiteur de recherche indépendant - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche
Superviseur⋅e principal⋅e :
Maîtrise recherche - UdeM
Maîtrise professionnelle - UdeM
Visiteur de recherche indépendant - Technical University of Munich
Doctorat - École Polytechnique Fédérale de Lausanne
Collaborateur·rice de recherche
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - Valence
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)
Superviseur⋅e principal⋅e :
Maîtrise professionnelle - UdeM
Collaborateur·rice alumni - UdeM
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - McGill
Superviseur⋅e principal⋅e :

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.… (voir plus) 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 … (voir 10 de plus)
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… (voir plus)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… (voir plus)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… (voir plus)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… (voir plus)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