Portrait of Aaron Courville

Aaron Courville

Core Academic Member
Canada CIFAR AI Chair
Associate Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Computer Vision
Deep Learning
Generative Models
Natural Language Processing
Reinforcement Learning
Representation Learning

Biography

Aaron Courville is a professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He has a PhD from the Robotics Institute, Carnegie Mellon University.

Courville was an early contributor to deep learning: he is a founding member of Mila – Quebec Artificial Intelligence Institute, a fellow in CIFAR’s Learning in Machines & Brains program and, with Ian Goodfellow and Yoshua Bengio, co-wrote the seminal textbook on deep learning.

His current research focuses on the development of deep learning models and methods. He is particularly interested in reinforcement learning, deep generative models and multimodal ML, as well as their applications, such as computer vision and natural language processing.

Courville holds a Canada CIFAR AI Chair and a Canada Research Chair in Learning Representations that Generalize Systematically. His research has been supported by Microsoft Research, Samsung, Hitachi, Sony and Google (Focused Research Award).

Current Students

PhD - Université de Montréal
PhD - Université de Montréal
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Undergraduate - Université de Montréal
Master's Research - Université de Montréal
PhD - Université de Montréal
Master's Research - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
Research Intern - Ghent University
PhD - Université de Montréal
PhD - Université de Montréal
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PhD - Université de Montréal
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Master's Research - Université de Montréal
PhD - Université de Montréal
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PhD - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
Master's Research - Université de Montréal
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PhD - Université de Montréal
PhD - Université de Montréal
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Publications

V-STaR: Training Verifiers for Self-Taught Reasoners
Arian Hosseini
Xingdi Yuan
Nikolay Malkin
Rishabh Agarwal
Common self-improvement approaches for large language models (LLMs), such as STaR (Zelikman et al., 2022), iteratively fine-tune LLMs on sel… (see more)f-generated solutions to improve their problem-solving ability. However, these approaches discard the large amounts of incorrect solutions generated during this process, potentially neglecting valuable information in such solutions. To address this shortcoming, we propose V-STaR that utilizes both the correct and incorrect solutions generated during the self-improvement process to train a verifier using DPO that judges correctness of model-generated solutions. This verifier is used at inference time to select one solution among many candidate solutions. Running V-STaR for multiple iterations results in progressively better reasoners and verifiers, delivering a 4% to 17% test accuracy improvement over existing self-improvement and verification approaches on common code generation and math reasoning benchmarks with LLaMA2 models.
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
Dinghuai Zhang
Ricky T. Q. Chen
Cheng-Hao Liu
LOQA: Learning with Opponent Q-Learning Awareness
Milad Aghajohari
Juan Agustin Duque
Tim Cooijmans
In various real-world scenarios, interactions among agents often resemble the dynamics of general-sum games, where each agent strives to opt… (see more)imize its own utility. Despite the ubiquitous relevance of such settings, decentralized machine learning algorithms have struggled to find equilibria that maximize individual utility while preserving social welfare. In this paper we introduce Learning with Opponent Q-Learning Awareness (LOQA) , a novel reinforcement learning algorithm tailored to optimizing an agent's individual utility while fostering cooperation among adversaries in partially competitive environments. LOQA assumes that each agent samples actions proportionally to their action-value function Q. Experimental results demonstrate the effectiveness of LOQA at achieving state-of-the-art performance in benchmark scenarios such as the Iterated Prisoner's Dilemma and the Coin Game. LOQA achieves these outcomes with a significantly reduced computational footprint compared to previous works, making it a promising approach for practical multi-agent applications.
The Curse of Diversity in Ensemble-Based Exploration
Zhixuan Lin
Pierluca D'Oro
Evgenii Nikishin
We uncover a surprising phenomenon in deep reinforcement learning: training a diverse ensemble of data-sharing agents -- a well-established … (see more)exploration strategy -- can significantly impair the performance of the individual ensemble members when compared to standard single-agent training. Through careful analysis, we attribute the degradation in performance to the low proportion of self-generated data in the shared training data for each ensemble member, as well as the inefficiency of the individual ensemble members to learn from such highly off-policy data. We thus name this phenomenon *the curse of diversity*. We find that several intuitive solutions -- such as a larger replay buffer or a smaller ensemble size -- either fail to consistently mitigate the performance loss or undermine the advantages of ensembling. Finally, we demonstrate the potential of representation learning to counteract the curse of diversity with a novel method named Cross-Ensemble Representation Learning (CERL) in both discrete and continuous control domains. Our work offers valuable insights into an unexpected pitfall in ensemble-based exploration and raises important caveats for future applications of similar approaches.
In deep reinforcement learning, a pruned network is a good network
Johan Samir Obando Ceron
Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage pri… (see more)or insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks and exhibit a type of"scaling law", using only a small fraction of the full network parameters.
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Max Schwarzer
Jesse Farebrother
Joshua Greaves
Ekin Dogus Cubuk
Rishabh Agarwal
Sergei V. Kalinin
Igor Mordatch
Kevin M Roccapriore
We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimu… (see more)lated by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.
Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Max Schwarzer
Jesse Farebrother
Joshua Greaves
Kevin Roccapriore
Ekin Dogus Cubuk
Rishabh Agarwal
Sergei Kalinin
Igor Mordatch
We introduce a machine learning approach to determine the transition rates of silicon atoms on a single layer of carbon atoms, when stimulat… (see more)ed by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition rates. These rates are then applied to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.
Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Max Schwarzer
Jesse Farebrother
Joshua Greaves
Kevin Roccapriore
Ekin Dogus Cubuk
Rishabh Agarwal
Sergei Kalinin
Igor Mordatch
We introduce a machine learning approach to determine the transition rates of silicon atoms on a single layer of carbon atoms, when stimulat… (see more)ed by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition rates. These rates are then applied to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.
Sparse Universal Transformer
Shawn Tan
Yikang Shen
Zhenfang Chen
Chuang Gan
The Universal Transformer (UT) is a variant of the Transformer that shares parameters across its layers and is Turing-complete under certain… (see more) assumptions. Empirical evidence also shows that UTs have better compositional generalization than Vanilla Transformers (VTs) in formal language tasks. The parameter-sharing also affords it better parameter efficiency than VTs. Despite its many advantages, most state-of-the-art NLP systems use VTs as their backbone model instead of UTs. This is mainly because scaling UT parameters is more compute and memory intensive than scaling up a VT. This paper proposes the Sparse Universal Transformer (SUT), which leverages Sparse Mixture of Experts (SMoE) to reduce UT's computation complexity while retaining its parameter efficiency and generalization ability. Experiments show that SUT combines the best of both worlds, achieving strong generalization results on formal language tasks (Logical inference and CFQ) and impressive parameter and computation efficiency on standard natural language benchmarks like WMT'14.
Double Gumbel Q-Learning.
David Yu-Tung Hui
Group Robust Classification Without Any Group Information
Christos Tsirigotis
Joao Monteiro
Pau Rodriguez
David Vazquez
Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings
Yi Ren
Samuel Lavoie
Mikhail Galkin
Danica J. Sutherland