Yoshua Bengio

jamal.abouhaibeh@mila.quebec

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

Collaborating researcher - Cambridge University

Principal supervisor :

David Rolnick

berkes.anais@mila.quebec

sanghyeok.choi@mila.quebec

Rim Assouel

PhD - Université de Montréal

assouelr@mila.quebec

Xiaoyin Chen

PhD - Université de Montréal

xiaoyin.chen@mila.quebec

Sanghyeok Choi

Collaborating researcher - KAIST

PhD - Université de Montréal

aniket.didolkar@mila.quebec

PhD - Université de Montréal

Co-supervisor :

Guillaume Lajoie

eric.elmoznino@mila.quebec

PhD - Université de Montréal

lena-nehale.ezzine@mila.quebec

Jean-Pierre Falet

PhD - Université de Montréal

jean-pierre.falet@mila.quebec

PhD

pietro.greiner@mila.quebec

Mohsin Hasan

PhD - Université de Montréal

mohsin.hasan@mila.quebec

thomas.jiralerspong@mila.quebec

Moksh Jain

PhD - Université de Montréal

moksh.jain@mila.quebec

PhD - Université de Montréal

Principal supervisor :

Guillaume Lajoie

Collaborating Alumni - Université de Montréal

younesse.kaddar@mila.quebec

Hyeonah Kim

Postdoctorate - Université de Montréal

Principal supervisor :

Alex Hernández-García

hyeonah.kim@mila.quebec

Minsu Kim

Collaborating researcher - Université de Montréal

minsu.kim@mila.quebec

Collaborating researcher - KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

jeongjae.lee@mila.quebec

cristian-dragos.manta@mila.quebec

Song LIU

Collaborating researcher - s.o.

Collaborating researcher

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

nguyenph@mila.quebec

Ali Parviz

Collaborating researcher - Ying Wu Coll of Computing

ali.parviz@mila.quebec

Lena Podina

Collaborating researcher - University of Waterloo

Principal supervisor :

David Rolnick

elena.podina@mila.quebec

jarrid.rector-brooks@mila.quebec

Jarrid Rector-Brooks

PhD - Université de Montréal

Danyal REHMAN

Postdoctorate - Université de Montréal

danyal.rehman@mila.quebec

oliver.richardson@mila.quebec

Oli RICHARDSON

Postdoctorate - Université de Montréal

Camille Rochefort-Boulanger

PhD - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

Co-supervisor :

Alex Hernández-García

divya.sharma@mila.quebec

Mélisande Astrid Crystal Teng

Collaborating Alumni - Université de Montréal

Co-supervisor :

Alex Tong

Collaborating Alumni - Université de Montréal

alexander.tong@mila.quebec

Collaborating researcher - University of Cambridge

oszkar.urban@mila.quebec

donna.vakalis@mila.quebec

Donna Vakalis

Collaborating Alumni - Université de Montréal

Co-supervisor :

David Rolnick

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

siddarth.venkatraman@mila.quebec

Collaborating researcher

joseph.viviano2@mila.quebec

Collaborating researcher - Université de Montréal

esmeralda.whitammer@mila.quebec

Collaborating Alumni

omar.younis@mila.quebec

Collaborating researcher - Université de Montréal

taeyoung.yun@mila.quebec

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Tianyu Zhang

PhD - Université de Montréal

tianyu.zhang@mila.quebec

PhD - McGill University

Principal supervisor :

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

Trajectory balance: Improved credit assignment in GFlowNets

Esmeralda S. Whitammer

Moksh Jain

Emmanuel Bengio

Chen Sun

Generative flow networks (GFlowNets) are a method for learning a stochastic policy for generating compositional objects, such as graphs or s… (see more)trings, from a given unnormalized density by sequences of actions, where many possible action sequences may lead to the same object. We find previously proposed learning objectives for GFlowNets, flow matching and detailed balance, which are analogous to temporal difference learning, to be prone to inefficient credit propagation across long action sequences. We thus propose a new learning objective for GFlowNets, trajectory balance, as a more efficient alternative to previously used objectives. We prove that any global minimizer of the trajectory balance objective can define a policy that samples exactly from the target distribution. In experiments on four distinct domains, we empirically demonstrate the benefits of the trajectory balance objective for GFlowNet convergence, diversity of generated samples, and robustness to long action sequences and large action spaces.

2022-11-28

Neural Information Processing Systems (Accept)

Weakly Supervised Representation Learning with Sparse Perturbations

Kartik Ahuja

Jason Hartford

The theory of representation learning aims to build methods that provably invert the data generating process with minimal domain knowledge o… (see more)r any source of supervision. Most prior approaches require strong distributional assumptions on the latent variables and weak supervision (auxiliary information such as timestamps) to provide provable identification guarantees. In this work, we show that if one has weak supervision from observations generated by sparse perturbations of the latent variables--e.g. images in a reinforcement learning environment where actions move individual sprites--identification is achievable under unknown continuous latent distributions. We show that if the perturbations are applied only on mutually exclusive blocks of latents, we identify the latents up to those blocks. We also show that if these perturbation blocks overlap, we identify latents up to the smallest blocks shared across perturbations. Consequently, if there are blocks that intersect in one latent variable only, then such latents are identified up to permutation and scaling. We propose a natural estimation procedure based on this theory and illustrate it on low-dimensional synthetic and image-based experiments.

2022-11-28

Neural Information Processing Systems (Accept)

Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection

Bonaventure F. P. Dossou

Dianbo Liu

Xu Ji

Moksh Jain

Almer M. van der Sloot

Roger Palou

Michael Tyers

As antibiotic-resistant bacterial strains are rapidly spreading worldwide, infections caused by these strains are emerging as a global crisi… (see more)s causing the death of millions of people every year. Antimicrobial Peptides (AMPs) are one of the candidates to tackle this problem because of their potential diversity, and ability to favorably modulate the host immune response. However, large-scale screening of new AMP candidates is expensive, time-consuming, and now affordable in developing countries, which need the treatments the most. In this work, we propose a novel active machine learning-based framework that statistically minimizes the number of wet-lab experiments needed to design new AMPs, while ensuring a high diversity and novelty of generated AMPs sequences, in multi-rounds of wet-lab AMP screening settings. Combining recurrent neural network models and a graph-based filter (GraphCC), our proposed approach delivers novel and diverse candidates and demonstrates better performances according to our defined metrics.

2022-11-09

Research Square (Research Square) (accepted)

Object-centric causal representation learning

Amin Mansouri

Jason Hartford

Kartik Ahuja

2022-11-06

NeurIPS.cc/2022/Workshop/NeurReps (poster)

Laurence Perreault-Levasseur

Posterior samples of source galaxies in strong gravitational lenses with score-based priors

Alexandre Adam

Adam Coogan

Nikolay Malkin

Ronan Legin

Yashar Hezaveh

Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge,… (see more) in part due to the difficulties of accurately quantifying the priors. Here, we report the use of a score-based model to encode the prior for the inference of undistorted images of background galaxies. This model is trained on a set of high-resolution images of undistorted galaxies. By adding the likelihood score to the prior score and using a reverse-time stochastic differential equation solver, we obtain samples from the posterior. Our method produces independent posterior samples and models the data almost down to the noise level. We show how the balance between the likelihood and the prior meet our expectations in an experiment with out-of-distribution data.

2022-11-06

ArXiv (preprint)

Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes

Mizu Nishikawa-Toomey

Tristan Deleu

Jithendaraa Subramanian

Laurent Charlin

Bayesian causal structure learning aims to learn a posterior distribution over directed acyclic graphs (DAGs), and the mechanisms that defin… (see more)e the relationship between parent and child variables. By taking a Bayesian approach, it is possible to reason about the uncertainty of the causal model. The notion of modelling the uncertainty over models is particularly crucial for causal structure learning since the model could be unidentifiable when given only a finite amount of observational data. In this paper, we introduce a novel method to jointly learn the structure and mechanisms of the causal model using Variational Bayes, which we call Variational Bayes-DAG-GFlowNet (VBG). We extend the method of Bayesian causal structure learning using GFlowNets to learn not only the posterior distribution over the structure, but also the parameters of a linear-Gaussian model. Our results on simulated data suggest that VBG is competitive against several baselines in modelling the posterior over DAGs and mechanisms, while offering several advantages over existing methods, including the guarantee to sample acyclic graphs, and the flexibility to generalize to non-linear causal mechanisms.

2022-11-03

arXiv (preprint)

A General-Purpose Neural Architecture for Geospatial Systems

Nasim Rahaman

Martin Weiss

Frederik Träuble

Francesco Locatello

Alexandre Lacoste

Chris Pal

Li Erran Li

Bernhard Schölkopf

2022-11-01

OpenReview (unknown)

Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions

Chanakya Ajit Ekbote

Moksh J. Jain

Payel Das

Generative Flow Networks (GFlowNets) have demonstrated significant performance improvements for generating diverse discrete objects …

2022-10-31

arXiv (preprint)

Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning

Hongyu Zang

Xin Li

Romain Laroche

Remi Tachet des Combes

2022-10-31

arXiv (preprint)

Efficient Queries Transformer Neural Processes

Leo Feng

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

2022-10-20

NeurIPS.cc/2022/Workshop/MetaLearn (poster)

FL Games: A federated learning framework for distribution shifts

Niladri Chatterjee

Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However… (see more), participating clients typically each hold data from a different distribution, whereby predictive models with strong in-distribution generalization can fail catastrophically on unseen domains. In this work, we argue that in order to generalize better across non-i.i.d. clients, it is imperative to only learn correlations that are stable and invariant across domains. We propose FL Games, a game-theoretic framework for federated learning for learning causal features that are invariant across clients. While training to achieve the Nash equilibrium, the traditional best response strategy suffers from high-frequency oscillations. We demonstrate that FL Games effectively resolves this challenge and exhibits smooth performance curves. Further, FL Games scales well in the number of clients, requires significantly fewer communication rounds, and is agnostic to device heterogeneity. Through empirical evaluation, we demonstrate that FL Games achieves high out-of-distribution performance on various benchmarks.

2022-10-20

Federated Learning @ Neural Information Processing Systems (oral)

Inductive biases for deep learning of higher-level cognition

Anirudh Goyal

2022-10-11

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences (published)