Yoshua Bengio

Biography

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

For more information please contact Marie-Josée Beauchamp, Administrative Assistant at marie-josee.beauchamp@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 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

Mohammed Abukalam

Collaborating Alumni - Université de Montréal

agassoussisalwane2@gmail.com

Salwane Agassoussi

Université de Montréal

Berkes Anaïs

Collaborating researcher - Cambridge University

Principal supervisor :

Rim Assouel

PhD - Université de Montréal

Ayoub Atanane

Collaborating Alumni - Université du Québec à Rimouski

Stefan Bauer

Independent visiting researcher

Co-supervisor :

Guillaume Lajoie

Paul Bertin

PhD - Université de Montréal

Ghait Boukachab

Collaborating Alumni - UQAR

Shahana Chatterjee

Collaborating researcher - N/A

Principal supervisor :

David Rolnick

Xiaoyin Chen

PhD - Université de Montréal

Sanghyeok Choi

Collaborating researcher - KAIST

PhD - Université de Montréal

PhD - Université de Montréal

Collaborating Alumni - Université de Montréal

Eric Elmoznino

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Léna Ezzine

PhD - Université de Montréal

Jean-Pierre Falet

PhD - Université de Montréal

Co-supervisor :

Leo Feng

PhD - Université de Montréal

Research Intern - Université de Montréal

Ivan Grega

Research Intern - Université de Montréal

Pietro Greiner

PhD

Mohsin Hasan

PhD - Université de Montréal

mohsin.hasan@mila.quebec

Edward Hu

PhD - Université de Montréal

Moksh Jain

PhD - Université de Montréal

moksh.jain@mila.quebec

Master's Research - Université de Montréal

Co-supervisor :

Collaborating Alumni - Université de Montréal

Minsu Kim

Research Intern - Université de Montréal

Collaborating researcher - Université de Montréal

Michał Koziarski

Collaborating Alumni - Université de Montréal

Salem Lahlou

Collaborating Alumni - Université de Montréal

Seanie Lee

Collaborating Alumni - Université de Montréal

Postdoctorate - Université de Montréal

Principal supervisor :

Collaborating Alumni

Zhen Liu

Collaborating Alumni - Université de Montréal

Principal supervisor :

Liam Paull

Matt MacDermott

Collaborating Alumni - Imperial College London

PhD - Université de Montréal

Mohammed Mahfoud

Collaborating Alumni - Université de Montréal

Nikolay Malkin

Collaborating Alumni - Université de Montréal

Cristian Dragos Manta

PhD - Université de Montréal

Co-supervisor :

Dhanya Sridhar

Sören Mindermann

Collaborating researcher - Université de Montréal

Sarthak Mittal

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

Postdoctorate - 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

PhD - University of Waterloo

Principal supervisor :

David Rolnick

Nassim Rahaman

Collaborating Alumni - Max-Planck-Institute for Intelligent Systems

Jarrid Rector-Brooks

PhD - Université de Montréal

Danyal REHMAN

Postdoctorate - Université de Montréal

James Requeima

Independent visiting researcher - Université de Montréal

Oli RICHARDSON

Postdoctorate - Université de Montréal

Camille Rochefort-Boulanger

PhD - Université de Montréal

Principal supervisor :

Julie Hussin

Victor Schmidt

Collaborating Alumni - Université de Montréal

Postdoctorate - Université de Montréal

Master's Research - Université de Montréal

Marcin Sendera

Collaborating Alumni - Université de Montréal

Dounia Shaaban Kabakibo

Research Intern - Université de Montréal

Vedant Shah

Master's Research - Université de Montréal

Postdoctorate

Marco Stock

Independent visiting researcher - Technical University of Munich

marco.stock@tum.de

Mélisande Astrid Crystal Teng

PhD - Université de Montréal

Co-supervisor :

Collaborating researcher - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)

Principal supervisor :

David Rolnick

alexander.tong@mila.quebec

Alex Tong

Postdoctorate - Université de Montréal

Postdoctorate - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Principal supervisor :

Collaborating researcher - Université de Montréal

Zichao Yan

Collaborating Alumni - Université de Montréal

Omar G. Younis

Collaborating researcher

Collaborating researcher - KAIST

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

PhD - 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

Zero-Shot Object-Centric Representation Learning

Aniket Rajiv Didolkar

Andrii Zadaianchuk

Anirudh Goyal

Michael Curtis Mozer

Georg Martius

Maximilian Seitzer

The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities… (see more). Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing pre-trained self-supervised features. However, so far, object-centric methods have mostly been applied in-distribution, with models trained and evaluated on the same dataset. This is in contrast to the wider trend in machine learning towards general-purpose models directly applicable to unseen data and tasks. Thus, in this work, we study current object-centric methods through the lens of zero-shot generalization by introducing a benchmark comprising eight different synthetic and real-world datasets. We analyze the factors influencing zero-shot performance and find that training on diverse real-world images improves transferability to unseen scenarios. Furthermore, inspired by the success of task-specific fine-tuning in foundation models, we introduce a novel fine-tuning strategy to adapt pre-trained vision encoders for the task of object discovery. We find that the proposed approach results in state-of-the-art performance for unsupervised object discovery, exhibiting strong zero-shot transfer to unseen datasets.

2024-08-17

ArXiv (preprint)

Zero-Shot Object-Centric Representation Learning

Aniket Rajiv Didolkar

Andrii Zadaianchuk

Anirudh Goyal

Michael Curtis Mozer

Georg Martius

Maximilian Seitzer

2024-08-17

ArXiv (preprint)

Can a Bayesian Oracle Prevent Harm from an Agent?

Michael K. Cohen

Nikolay Malkin

Matt MacDermott

Damiano Fornasiere

Pietro Greiner

Younesse Kaddar

Is there a way to design powerful AI systems based on machine learning methods that would satisfy probabilistic safety guarantees? With the … (see more)long-term goal of obtaining a probabilistic guarantee that would apply in every context, we consider estimating a context-dependent bound on the probability of violating a given safety specification. Such a risk evaluation would need to be performed at run-time to provide a guardrail against dangerous actions of an AI. Noting that different plausible hypotheses about the world could produce very different outcomes, and because we do not know which one is right, we derive bounds on the safety violation probability predicted under the true but unknown hypothesis. Such bounds could be used to reject potentially dangerous actions. Our main results involve searching for cautious but plausible hypotheses, obtained by a maximization that involves Bayesian posteriors over hypotheses. We consider two forms of this result, in the iid case and in the non-iid case, and conclude with open problems towards turning such theoretical results into practical AI guardrails.

2024-08-09

ArXiv (preprint)

Cell Morphology-Guided Small Molecule Generation with GFlowNets

Stephen Zhewen Lu

Ziqing Lu

Ehsan Hajiramezanali

Tommaso Biancalani

Gabriele Scalia

Michał Koziarski

High-content phenotypic screening, including high-content imaging (HCI), has gained popularity in the last few years for its ability to char… (see more)acterize novel therapeutics without prior knowledge of the protein target. When combined with deep learning techniques to predict and represent molecular-phenotype interactions, these advancements hold the potential to significantly accelerate and enhance drug discovery applications. This work focuses on the novel task of HCI-guided molecular design. Generative models for molecule design could be guided by HCI data, for example with a supervised model that links molecules to phenotypes of interest as a reward function. However, limited labeled data, combined with the high-dimensional readouts, can make training these methods challenging and impractical. We consider an alternative approach in which we leverage an unsupervised multimodal joint embedding to define a latent similarity as a reward for GFlowNets. The proposed model learns to generate new molecules that could produce phenotypic effects similar to those of the given image target, without relying on pre-annotated phenotypic labels. We demonstrate that the proposed method generates molecules with high morphological and structural similarity to the target, increasing the likelihood of similar biological activity, as confirmed by an independent oracle model.

2024-08-09

ArXiv (preprint)

Open Problems in Technical AI Governance

Anka Reuel

Benjamin Bucknall

Stephen Casper

Tim Fist

Lisa Soder

Onni Aarne

Lewis Hammond

Lujain Ibrahim

Alan Chan

Peter Wills

Markus Anderljung

Ben Garfinkel

Lennart Heim

Andrew Trask

Gabriel Mukobi

Rylan Schaeffer

Mauricio Baker

Sara Hooker

Irene Solaiman

Alexandra Luccioni … (see 11 more)

Nitarshan Rajkumar

Nicolas Moes

Jeffrey Ladish

Neel Guha

Jessica Newman

Tobin South

Alex Pentland

Sanmi Koyejo

Mykel Kochenderfer

Robert F. Trager

AI progress is creating a growing range of risks and opportunities, but it is often unclear how they should be navigated. In many cases, the… (see more) barriers and uncertainties faced are at least partly technical. Technical AI governance, referring to technical analysis and tools for supporting the effective governance of AI, seeks to address such challenges. It can help to (a) identify areas where intervention is needed, (b) identify and assess the efficacy of potential governance actions, and (c) enhance governance options by designing mechanisms for enforcement, incentivization, or compliance. In this paper, we explain what technical AI governance is, why it is important, and present a taxonomy and incomplete catalog of its open problems. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.

2024-07-20

ArXiv (preprint)

Redesigning Information Markets in the Era of Language Models

Martin Weiss

Nasim Rahaman

Manuel Wüthrich

Li Erran Li

Bernhard Schölkopf

Chris Pal

2024-07-10

colmweb.org/COLM/2024/Conference (accepted)

Improving Gradient-Guided Nested Sampling for Posterior Inference

Pablo Lemos

Nikolay Malkin

Will Handley

Laurence Perreault-Levasseur

Yashar Hezaveh

We present a performant, general-purpose gradient-guided nested sampling (GGNS) algorithm, combining the state of the art in differentiable … (see more)programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization. This unique combination allows GGNS to scale well with dimensionality and perform competitively on a variety of synthetic and real-world problems. We also show the potential of combining nested sampling with generative flow networks to obtain large amounts of high-quality samples from the posterior distribution. This combination leads to faster mode discovery and more accurate estimates of the partition function.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (published)

Memory Efficient Neural Processes via Constant Memory Attention Block

Leo Feng

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (published)

proceedings.mlr.press

On Generalization for Generative Flow Networks

Anas Krichel

Nikolay Malkin

Salem Lahlou

Generative Flow Networks (GFlowNets) have emerged as an innovative learning paradigm designed to address the challenge of sampling from an u… (see more)nnormalized probability distribution, called the reward function. This framework learns a policy on a constructed graph, which enables sampling from an approximation of the target probability distribution through successive steps of sampling from the learned policy. To achieve this, GFlowNets can be trained with various objectives, each of which can lead to the model s ultimate goal. The aspirational strength of GFlowNets lies in their potential to discern intricate patterns within the reward function and their capacity to generalize effectively to novel, unseen parts of the reward function. This paper attempts to formalize generalization in the context of GFlowNets, to link generalization with stability, and also to design experiments that assess the capacity of these models to uncover unseen parts of the reward function. The experiments will focus on length generalization meaning generalization to states that can be constructed only by longer trajectories than those seen in training.

2024-07-03

ArXiv (preprint)

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold

Lazar Atanackovic

Xi Zhang

Brandon Amos

Mathieu Blanchette

Leo J Lee

Alexander Tong

Kirill Neklyudov

Numerous biological and physical processes can be modeled as systems of interacting samples evolving continuously over time, e.g. the dynami… (see more)cs of communicating cells or physical particles. Flow-based models allow for learning these dynamics at the population level --- they model the evolution of the entire distribution of samples. However, current flow-based models are limited to a single initial population and a set of predefined conditions which describe different dynamics. We propose

2024-06-17

ICML.cc/2024/Workshop/GRaM (published)

Cell Morphology-Guided Small Molecule Generation with GFlowNets

Stephen Zhewen Lu

Ziqing Lu

Ehsan Hajiramezanali

Tommaso Biancalani

Gabriele Scalia

Michał Koziarski

2024-06-16

ICML.cc/2024/Workshop/AI4Science (poster)

MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation

Lu Li

Tianyu Zhang

Zhiqi Bu

Suyuchen Wang

Huan He

Jie Fu

Yonghui Wu

Jiang Bian

Yong Chen

Model merging has emerged as an effective approach to combine multiple single-task models into a multitask model. This process typically inv… (see more)olves computing a weighted average of the model parameters without any additional training. Existing model-merging methods focus on enhancing average task accuracy. However, interference and conflicts between the objectives of different tasks can lead to trade-offs during the merging process. In real-world applications, a set of solutions with various trade-offs can be more informative, helping practitioners make decisions based on diverse preferences. In this paper, we introduce a novel and low-compute algorithm, Model Merging with Amortized Pareto Front (MAP). MAP efficiently identifies a Pareto set of scaling coefficients for merging multiple models, reflecting the trade-offs involved. It amortizes the substantial computational cost of evaluations needed to estimate the Pareto front by using quadratic approximation surrogate models derived from a pre-selected set of scaling coefficients. Experimental results on vision and natural language processing tasks demonstrate that MAP can accurately identify the Pareto front, providing practitioners with flexible solutions to balance competing task objectives. We also introduce Bayesian MAP for scenarios with a relatively low number of tasks and Nested MAP for situations with a high number of tasks, further reducing the computational cost of evaluation.

2024-06-11

ArXiv (preprint)