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
Founder and Scientific Advisor, Leadership Team
Research Topics
Causality
Computational Neuroscience
Deep Learning
Generative Models
Graph Neural Networks
Machine Learning Theory
Medical Machine Learning
Molecular Modeling
Natural Language Processing
Probabilistic Models
Reasoning
Recurrent Neural Networks
Reinforcement Learning
Representation Learning
Website
Google Scholar

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
Github
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Berkes Anaïs
Collaborating researcher - Cambridge University
Principal supervisor :
David Rolnick
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Rim Assouel
PhD - Université de Montréal
Stefan Bauer
Independent visiting researcher
Co-supervisor :
Guillaume Lajoie
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Shahana Chatterjee
Collaborating researcher - N/A
Principal supervisor :
David Rolnick
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LinkedIn
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Xiaoyin Chen
PhD - Université de Montréal
Sanghyeok Choi
Collaborating researcher - KAIST
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Aniket Didolkar
PhD - Université de Montréal
Website
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Abdessamad EL KABID
Collaborating Alumni - Université de Montréal
Co-supervisor :
Loubna Benabbou
Desmond Elliott
Independent visiting researcher
Principal supervisor :
Siva Reddy
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Eric Elmoznino
PhD - Université de Montréal
Co-supervisor :
Guillaume Lajoie
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Léna Ezzine
PhD - Université de Montréal
Jean-Pierre Falet
PhD - Université de Montréal
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Pietro Greiner
PhD
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Mohsin Hasan
PhD - Université de Montréal
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Moksh Jain
PhD - Université de Montréal
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Thomas Jiralerspong
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
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Younesse Kaddar
Collaborating Alumni - Université de Montréal
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Hyeonah Kim
Postdoctorate - Université de Montréal
Principal supervisor :
Alex Hernández-García
Tabitha Edith Lee
Postdoctorate - Université de Montréal
Principal supervisor :
Glen Berseth
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Chenghao Liu
Collaborating Alumni
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Nikolay Malkin
Collaborating Alumni - Université de Montréal
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Cristian Dragos Manta
PhD - Université de Montréal
Co-supervisor :
Dhanya Sridhar
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Sarthak Mittal
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Website
Github
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Phong Nguyen
Independent visiting researcher - Université de Montréal
Padideh Nouri
PhD - Université de Montréal
Principal supervisor :
Doina Precup
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Ali Parviz
Collaborating researcher - Ying Wu Coll of Computing
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Lena Podina
Collaborating researcher - University of Waterloo
Principal supervisor :
David Rolnick
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Nassim Rahaman
Collaborating Alumni - Max-Planck-Institute for Intelligent Systems
Amine RAZIG
Collaborating researcher - Université de Montréal
Co-supervisor :
Loubna Benabbou
Website
Jarrid Rector-Brooks
PhD - Université de Montréal
Danyal REHMAN
Postdoctorate - Université de Montréal
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Oli RICHARDSON
Postdoctorate - Université de Montréal
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Camille Rochefort-Boulanger
PhD - Université de Montréal
Principal supervisor :
Julie Hussin
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Dragos Secrieru
Collaborating Alumni - Université de Montréal
Divya Sharma
Postdoctorate
Co-supervisor :
Alex Hernández-García
Website
Github
Vincent Taboga
Collaborating Alumni - Polytechnique Montréal
Co-supervisor :
Pierre-Luc Bacon
Website
Github
Google Scholar
Mélisande Astrid Crystal Teng
PhD - Université de Montréal
Co-supervisor :
Hugo Larochelle
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Ivan Titov
Collaborating researcher
Principal supervisor :
Siva Reddy
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Alex Tong
Collaborating Alumni - Université de Montréal
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Donna Vakalis
Collaborating Alumni - Université de Montréal
Co-supervisor :
David Rolnick
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Siddarth Venkatraman
PhD - Université de Montréal
Principal supervisor :
Glen Berseth
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Omar G. Younis
Collaborating researcher
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Taeyoung YUN
Collaborating researcher - Université de Montréal
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Tianyu Zhang
PhD - Université de Montréal
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Nicole Zhang
PhD - McGill University
Principal supervisor :
Mathieu Blanchette
Website
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Dinghuai Zhang
PhD - Université de Montréal
Principal supervisor :
Aaron Courville
Website
Harry Zhao
Collaborating Alumni - McGill University
Principal supervisor :
Doina Precup
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Publications

Adaptive Teachers for Amortized Samplers
Minsu Kim
Sanghyeok Choi
Taeyoung YUN
Emmanuel Bengio
Leo Feng
Jarrid Rector-Brooks
Sungsoo Ahn
Jinkyoo Park
Nikolay Malkin
Yoshua Bengio
Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnorma… (see more)lized density where exact sampling is intractable. When sampling is implemented as a sequential decision-making process, reinforcement learning (RL) methods, such as generative flow networks, can be used to train the sampling policy. Off-policy RL training facilitates the discovery of diverse, high-reward candidates, but existing methods still face challenges in efficient exploration. We propose to use an adaptive training distribution (the \teacher) to guide the training of the primary amortized sampler (the \student). The \teacher, an auxiliary behavior model, is trained to sample high-loss regions of the \student and can generalize across unexplored modes, thereby enhancing mode coverage by providing an efficient training curriculum. We validate the effectiveness of this approach in a synthetic environment designed to present an exploration challenge, two diffusion-based sampling tasks, and four biochemical discovery tasks demonstrating its ability to improve sample efficiency and mode coverage. Source code is available at https://github.com/alstn12088/adaptive-teacher.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Minsu Kim
Sanghyeok Choi
Jiwoo Son
Hyeonah Kim
Jinkyoo Park
Yoshua Bengio
We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference an… (see more)d parallel stochastic search. Our method first leverages Generative Flow Networks (GFlowNets) to amortize a \emph{multi-modal} prior distribution over combinatorial solution space that encompasses both high-reward and diversified solutions. This prior is iteratively updated via parallel stochastic search in the spirit of Ant Colony Optimization (ACO), leading to the posterior distribution that generates near-optimal solutions. Extensive experiments across seven combinatorial optimization problems demonstrate GFACS's promising performances.
2025-01-21
aistats.org/AISTATS/2025/Conference (poster)
doi.org
proceedings.mlr.press
AssembleFlow: Rigid Flow Matching with Inertial Frames for Molecular Assembly
Hongyu Guo
Yoshua Bengio
Shengchao Liu
2025-01-21
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
BigDocs: An Open Dataset for Training Multi-modal Models on Document and Code Tasks
Juan Rodriguez
Xiangru Jian
Siba Smarak Panigrahi
Tianyu Zhang
Aarash Feizi
Abhay Puri
Akshay Kalkunte
François Savard
Ahmed Masry
Shravan Nayak
Rabiul Awal
Mahsa Massoud
Amirhossein Abaskohi
Zichao Li
Suyuchen Wang
Pierre-Andre Noel
Mats Leon Richter
Saverio Vadacchino
Shubham Agarwal
Sanket Biswas … (see 23 more)
Sara Shanian
Ying Zhang
Noah Bolger
Kurt MacDonald
Simon Fauvel
Sathwik Tejaswi
Srinivas Sunkara
Joao Monteiro
Krishnamurthy Dj Dvijotham
Torsten Scholak
Nicolas Chapados
Sepideh Kharagani
Sean Hughes
M. Özsu
Siva Reddy
Marco Pedersoli
Yoshua Bengio
Christopher Pal
Issam Laradji
Spandana Gella
Perouz Taslakian
David Vázquez
Sai Rajeswar
Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows,… (see more) extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io .
2025-01-21
International Conference on Learning Representations (poster)
doi.org
openreview.net
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets
Zhen Liu
Tim Z. Xiao
Weiyang Liu
Yoshua Bengio
Dinghuai Zhang
While one commonly trains large diffusion models by collecting datasets on target downstream tasks, it is often desired to align and finetun… (see more)e pretrained diffusion models with some reward functions that are either designed by experts or learned from small-scale datasets. Existing post-training methods for reward finetuning of diffusion models typically suffer from lack of diversity in generated samples, lack of prior preservation, and/or slow convergence in finetuning. In response to this challenge, we take inspiration from recent successes in generative flow networks (GFlowNets) and propose a reinforcement learning method for diffusion model finetuning, dubbed Nabla-GFlowNet (abbreviated as
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models
Seanie Lee
Haebin Seong
Dong Bok Lee
Minki Kang
Xiaoyin Chen
Dominik Wagner
Yoshua Bengio
Juho Lee
Sung Ju Hwang
Safety guard models that detect malicious queries aimed at large language models (LLMs) are essential for ensuring the secure and responsibl… (see more)e deployment of LLMs in real-world applications. However, deploying existing safety guard models with billions of parameters alongside LLMs on mobile devices is impractical due to substantial memory requirements and latency. To reduce this cost, we distill a large teacher safety guard model into a smaller one using a labeled dataset of instruction-response pairs with binary harmfulness labels. Due to the limited diversity of harmful instructions in the existing labeled dataset, naively distilled models tend to underperform compared to larger models. To bridge the gap between small and large models, we propose HarmAug, a simple yet effective data augmentation method that involves jailbreaking an LLM and prompting it to generate harmful instructions. Given a prompt such as, "Make a single harmful instruction prompt that would elicit offensive content", we add an affirmative prefix (e.g., "I have an idea for a prompt:") to the LLM's response. This encourages the LLM to continue generating the rest of the response, leading to sampling harmful instructions. Another LLM generates a response to the harmful instruction, and the teacher model labels the instruction-response pair. We empirically show that our HarmAug outperforms other relevant baselines. Moreover, a 435-million-parameter safety guard model trained with HarmAug achieves an F1 score comparable to larger models with over 7 billion parameters, and even outperforms them in AUPRC, while operating at less than 25% of their computational cost.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning
Seanie Lee
Minsu Kim
Lynn Cherif
David Dobre
Juho Lee
Sung Ju Hwang
Kenji Kawaguchi
Gauthier Gidel
Yoshua Bengio
Nikolay Malkin
Moksh Jain
Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of lar… (see more)ge language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit undesirable responses from a target LLM, as measured, for example, by an auxiliary toxicity classifier. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. As a flexible and probabilistically principled alternative, we propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts. We find that the attacks generated by our method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer well between target LLMs. Finally, we demonstrate that models safety-tuned using a dataset of red-teaming prompts generated by our method are robust to attacks from other RL-based red-teaming approaches.
2025-01-21
International Conference on Learning Representations (poster)
doi.org
openreview.net
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
Yoshua Bengio
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.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
Lazar Atanackovic
Xi Zhang
Brandon Amos
Mathieu Blanchette
Leo J. Lee
Yoshua Bengio
Alexander Tong
Kirill Neklyudov
Numerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynam… (see more)ics of communicating cells or physical particles. Learning the dynamics of such systems is essential for predicting the temporal evolution of populations across novel samples and unseen environments. 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 argue that multiple processes in natural sciences have to be represented as vector fields on the Wasserstein manifold of probability densities. That is, the change of the population at any moment in time depends on the population itself due to the interactions between samples. In particular, this is crucial for personalized medicine where the development of diseases and their respective treatment response depend on the microenvironment of cells specific to each patient. We propose Meta Flow Matching (MFM), a practical approach to integrate along these vector fields on the Wasserstein manifold by amortizing the flow model over the initial populations. Namely, we embed the population of samples using a Graph Neural Network (GNN) and use these embeddings to train a Flow Matching model. This gives MFM the ability to generalize over the initial distributions, unlike previously proposed methods. We demonstrate the ability of MFM to improve the prediction of individual treatment responses on a large-scale multi-patient single-cell drug screen dataset.
2025-01-21
International Conference on Learning Representations (poster)
doi.org
openreview.net
Structure Language Models for Protein Conformation Generation
Jiarui Lu
Xiaoyin Chen
Stephen Z. Lu
Chence Shi
Hongyu Guo
Yoshua Bengio
Jian Tang
Proteins adopt multiple structural conformations to perform their diverse biological functions, and understanding these conformations is cru… (see more)cial for advancing drug discovery. Traditional physics-based simulation methods often struggle with sampling equilibrium conformations and are computationally expensive. Recently, deep generative models have shown promise in generating protein conformations as a more efficient alternative. However, these methods predominantly rely on the diffusion process within a 3D geometric space, which typically centers around the vicinity of metastable states and is often inefficient in terms of runtime. In this paper, we introduce Structure Language Modeling (SLM) as a novel framework for efficient protein conformation generation. Specifically, the protein structures are first encoded into a compact latent space using a discrete variational auto-encoder, followed by conditional language modeling that effectively captures sequence-specific conformation distributions. This enables a more efficient and interpretable exploration of diverse ensemble modes compared to existing methods. Based on this general framework, we instantiate SLM with various popular LM architectures as well as proposing the ESMDiff, a novel BERT-like structure language model fine-tuned from ESM3 with masked diffusion. We verify our approach in various scenarios, including the equilibrium dynamics of BPTI, conformational change pairs, and intrinsically disordered proteins. SLM provides a highly efficient solution, offering a 20-100x speedup than existing methods in generating diverse conformations, shedding light on promising avenues for future research.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
On the Transfer of Object-Centric Representation Learning.
Aniket Rajiv Didolkar
Andrii Zadaianchuk
Anirudh Goyal
Michael Curtis Mozer
Yoshua Bengio
Georg Martius
Maximilian Seitzer
2025-01-21
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
Towards Improving Exploration Through Sibling Augmented GFlowNets
Kanika Madan
Alex Lamb
Emmanuel Bengio
Glen Berseth
Yoshua Bengio
2025-01-21
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net