Portrait de Laurent Charlin

Laurent Charlin

Membre académique principal
lcharlin@mila.quebec
Chaire en IA Canada-CIFAR
Professeur agrégé, HEC Montréal, Département de Sciences de la décision
Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage profond
Exploration des données
IA pour la science
Modèles génératifs
Modèles probabilistes
Recherche d'information
Réseaux de neurones en graphes
Systèmes de recommandation
Traitement du langage naturel
Google Scholar
LinkedIn
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Biographie

Laurent Charlin est titulaire d’une chaire en IA Canada-CIFAR et professeur agrégé à HEC Montréal. Il est également membre académique principal à Mila – Institut québécois d’intelligence artificielle.

Ses recherches portent sur le développement de nouveaux modèles d'apprentissage automatique pour aider à la prise de décision. Ses travaux récents concernent l'apprentissage à partir de données qui évoluent dans le temps. Il travaille également sur des applications dans des domaines tels que les systèmes de recommandation et l'optimisation.

Il est l'auteur de publications très citées sur les systèmes de dialogue (chatbots). Laurent Charlin a codéveloppé le Toronto Paper Matching System (TPMS), qui a été largement utilisé dans les conférences d'informatique pour faire correspondre les réviseur·euse·s aux articles. Il a également contribué à plusieurs MOOC récents, et a donné des conférences d'introduction et des interviews dans les médias pour contribuer au transfert de connaissances et améliorer la culture de l'IA.

Étudiants actuels

Publications

Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Pau Rodríguez
Massimo Caccia
Alexandre Lacoste
Lee Zamparo
Issam Laradji
Laurent Charlin
David Vázquez
Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying… (voir plus) more reliable machine-learning systems. In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction, providing details about the model's decision-making. Current methods tend to generate trivial counterfactuals about a model's decisions, as they often suggest to exaggerate or remove the presence of the attribute being classified. For the machine learning practitioner, these types of counterfactuals offer little value, since they provide no new information about undesired model or data biases. In this work, we identify the problem of trivial counterfactual generation and we propose DiVE to alleviate it. DiVE learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss to uncover multiple valuable explanations about the model's prediction. Further, we introduce a mechanism to prevent the model from producing trivial explanations. Experiments on CelebA and Synbols demonstrate that our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods. Code is available at https://github.com/ElementAI/beyond-trivial-explanations.
2021-09-30
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (publié)
doi.org
arxiv.org
Sequoia: A Software Framework to Unify Continual Learning Research
Fabrice Normandin
Florian Golemo
Oleksiy Ostapenko
Pau Rodríguez
Matthew Riemer
J. Hurtado
Lucas Cecchi
Khimya Khetarpal
Dominic Zhao
Ryan Lindeborg
Timothee LESORT
David Vázquez
Laurent Charlin
Irina Rish
Massimo Caccia
The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non… (voir plus)-stationary environments. In practice, a plethora of evaluation procedures (settings) and algorithmic solutions (methods) exist, each with their own potentially disjoint set of assumptions. This variety makes measuring progress in CL difficult. We propose a taxonomy of settings, where each setting is described as a set of assumptions. A tree-shaped hierarchy emerges from this view, where more general settings become the parents of those with more restrictive assumptions. This makes it possible to use inheritance to share and reuse research, as developing a method for a given setting also makes it directly applicable onto any of its children. We instantiate this idea as a publicly available software framework called Sequoia, which features a wide variety of settings from both the Continual Supervised Learning (CSL) and Continual Reinforcement Learning (CRL) domains. Sequoia also includes a growing suite of methods which are easy to extend and customize, in addition to more specialized methods from external libraries. We hope that this new paradigm and its first implementation can help unify and accelerate research in CL. You can help us grow the tree by visiting www.github.com/lebrice/Sequoia.
2021-08-01
ArXiv (prépublication)
doi.org
openreview.net
Comparative Study of Learning Outcomes for Online Learning Platforms
Francois St-Hilaire
Nathan J. Burns
Robert Belfer
Muhammad Shayan
Ariella Smofsky
Dung D. Vu
Antoine Frau
Joseph Potochny
Farid Faraji
Vincent Pavero
Neroli Ko
Ansona Onyi Ching
Sabina Elkins
A. Stepanyan
Adela Matajova
Laurent Charlin
Yoshua Bengio
Iulian V. Serban
Ekaterina Kochmar
2021-06-11
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
Pretraining Reward-Free Representations for Data-Efficient Reinforcement Learning
Max Schwarzer
Nitarshan Rajkumar
Michael Noukhovitch
Ankesh Anand
Laurent Charlin
Devon Hjelm
Philip Bachman
Aaron Courville
2021-03-08
International Conference on Learning Representations (inconnu)
openreview.net
openreview.net
DATA-EFFICIENT REINFORCEMENT LEARNING
Nitarshan Rajkumar
Michael Noukhovitch
Ankesh Anand
Laurent Charlin
R Devon Hjelm
Philip Bachman
Aaron Courville
Data efficiency poses a major challenge for deep reinforcement learning. We approach this issue from the perspective of self-supervised repr… (voir plus)esentation learning, leveraging reward-free exploratory data to pretrain encoder networks. We employ a novel combination of latent dynamics modelling and goal-reaching objectives, which exploit the inherent structure of data in reinforcement learning. We demonstrate that our method scales well with network capacity and pretraining data. When evaluated on the Atari 100k data-efficiency benchmark, our approach significantly outperforms previous methods combining unsupervised pretraining with task-specific finetuning, and approaches human-level performance.
2020-12-31
(publié)
www.semanticscholar.org
HADE: Hierarchical Affective Dialog Encoder for Personality Recognition in Conversation
Nabiha Asghar
P. Poupart
Jesse Hoey
Xin Jiang
M. Bradley
P. J. Lang
Affective
Kyunghyun Cho
Bart Van Merriënboer
Caglar Gul-666
Dzmitry Bahdanau
Holger Fethi Bougares
Pierre Colombo
Wojciech Witon
Ashutosh Modi
J. Kennedy
Mubbasir Kapadia
J. Devlin
Ming-Wei Chang
Kenton Lee … (voir 35 de plus)
Jennifer Golbeck
Cristina Robles
Xiaodong Gu
Kang Min
Yoo Jung-Woo
Iulian Serban
Alessandro Sordoni
Ryan Lowe
Laurent Charlin
Joelle Pineau
Aaron C. Courville
Alireza Souri
Shafigheh Hosseinpour
Amir Ma-822
Edward P Tighe
Jennifer C. Ureta
B. Andrei
Ashish Vaswani
Noam Shazeer
Niki Parmar
David I. Watson
Lee Anna
Clark. 1992
On
Thomas Wolf
Lysandre Debut
Julien Victor Sanh
Clement Chaumond
Anthony Delangue
Pier-339 Moi
Tim ric Cistac
Rémi Rault
Morgan Louf
Funtowicz
Julien Chaumond
2020-12-31
(publié)
www.semanticscholar.org
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning
Massimo Caccia
Pau Rodríguez
Oleksiy Ostapenko
Fabrice Normandin
Min Lin
Lucas Caccia
Issam Laradji
Irina Rish
Alexandre Lacoste
David Vázquez
Laurent Charlin
Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones. Two recent c… (voir plus)ontinual-learning scenarios have opened new avenues of research. In meta-continual learning, the model is pre-trained to minimize catastrophic forgetting of previous tasks. In continual-meta learning, the aim is to train agents for faster remembering of previous tasks through adaptation. In their original formulations, both methods have limitations. We stand on their shoulders to propose a more general scenario, OSAKA, where an agent must quickly solve new (out-of-distribution) tasks, while also requiring fast remembering. We show that current continual learning, meta-learning, meta-continual learning, and continual-meta learning techniques fail in this new scenario. We propose Continual-MAML, an online extension of the popular MAML algorithm as a strong baseline for this scenario. We empirically show that Continual-MAML is better suited to the new scenario than the aforementioned methodologies, as well as standard continual learning and meta-learning approaches.
2020-12-06
Conference on Neural Information Processing Systems (Accept (poster))
doi.org
arxiv.org
Synbols: Probing Learning Algorithms with Synthetic Datasets
Alexandre Lacoste
Pau Rodríguez
Frédéric Branchaud-Charron
Parmida Atighehchian
Massimo Caccia
Issam Laradji
Alexandre Drouin
Matt Craddock
Laurent Charlin
David Vázquez
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithm… (voir plus)s. Enabling the design of datasets to test specific properties and failure modes of learning algorithms is thus a problem of high interest, as it has a direct impact on innovation in the field. In this sense, we introduce Synbols -- Synthetic Symbols -- a tool for rapidly generating new datasets with a rich composition of latent features rendered in low resolution images. Synbols leverages the large amount of symbols available in the Unicode standard and the wide range of artistic font provided by the open font community. Our tool's high-level interface provides a language for rapidly generating new distributions on the latent features, including various types of textures and occlusions. To showcase the versatility of Synbols, we use it to dissect the limitations and flaws in standard learning algorithms in various learning setups including supervised learning, active learning, out of distribution generalization, unsupervised representation learning, and object counting.
2020-12-06
Conference on Neural Information Processing Systems (Accept (poster))
doi.org
arxiv.org
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles
Yao Lu
Yue Dong
Laurent Charlin
Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-sc… (voir plus)ale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and empirical results—using several state-of-the-art models trained on the Multi-XScience dataset—reveal that Multi-XScience is well suited for abstractive models.
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
On the Effectiveness of Two-Step Learning for Latent-Variable Models
Cem Subakan
Maxime Gasse
Laurent Charlin
Latent-variable generative models offer a principled solution for modeling and sampling from complex probability distributions. Implementing… (voir plus) a joint training objective with a complex prior, however, can be a tedious task, as one is typically required to derive and code a specific cost function for each new type of prior distribution. In this work, we propose a general framework for learning latent variable generative models in a two-step fashion. In the first step of the framework, we train an autoencoder, and in the second step we fit a prior model on the resulting latent distribution. This two-step approach offers a convenient alternative to joint training, as it allows for a straightforward combination of existing models without the hustle of deriving new cost functions, and the need for coding the joint training objectives. Through a set of experiments, we demonstrate that two-step learning results in performances similar to joint training, and in some cases even results in more accurate modeling.
2020-08-31
International Workshop on Machine Learning for Signal Processing (publié)
doi.org
A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM
Iulian V. Serban
Varun Gupta
Ekaterina Kochmar
Dung D. Vu
Robert Belfer
Joelle Pineau
Aaron Courville
Laurent Charlin
Yoshua Bengio
2020-06-09
Artificial Intelligence in Education (publié)
doi.org
arxiv.org
Inference for travel time on transportation networks
Mohamad Elmasri
Aurélie Labbe
Denis Larocque
Laurent Charlin
Travel time is essential for making travel decisions in real-world transportation networks. Understanding its distribution can resolve many … (voir plus)fundamental problems in transportation. Empirically, single-edge travel-time is well studied, but how to aggregate such information over many edges to arrive at the distribution of travel time over a route is still daunting. A range of statistical tools have been developed for network analysis; tools to study statistical behaviors of processes on dynamical networks are still lacking. This paper develops a novel statistical perspective to specific type of mixing ergodic processes (travel time), that mimic the behavior of travel time on real-world networks. Under general conditions on the single-edge speed (resistance) distribution, we show that travel time, normalized by distance, follows a Gaussian distribution with universal mean and variance parameters. We propose efficient inference methods for such parameters, and consequently asymptotic universal confidence and prediction intervals of travel time. We further develop path(route)-specific parameters that enable tighter Gaussian-based prediction intervals. We illustrate our methods with a real-world case study using mobile GPS data, where we show that the route-specific and universal intervals both achieve the 95\% theoretical coverage levels. Moreover, the route-specific prediction intervals result in tighter bounds that outperform competing models.
2020-04-22
(publié)
www.semanticscholar.org