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

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

Neda Adl

Maîtrise recherche - HEC

Anirudh Buvanesh

Doctorat - UdeM

Co-superviseur⋅e :

Aaron Courville

Github

Félix Gauthier

Maîtrise recherche - HEC

Soraya Ghassemlou

Maîtrise recherche - McGill

Site web

Github

Nicolas Goulet

Doctorat - HEC

Superviseur⋅e principal⋅e :

Eva Portelance

Google Scholar

Shubham Gupta

Doctorat - Université Laval

Superviseur⋅e principal⋅e :

Cem Subakan

Ben Hudson

Doctorat - UdeM

Co-superviseur⋅e :

Mizu Nishikawa-Toomey

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Collaborateur·rice alumni - UdeM

Doctorat - UdeM

Gaurav Sahu

Postdoctorat - HEC

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - UdeM

Publications

A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

Iulian V. Serban

Varun Gupta

Ekaterina Kochmar

Dung D. Vu

Robert Belfer

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

Prediction intervals for travel time on transportation networks

Mohamad Elmasri

Aurélie Labbe

Denis Larocque

Laurent Charlin

Estimating travel-time is essential for making travel decisions in transportation networks. Empirically, single road-segment travel-time is … (voir plus)well studied, but how to aggregate such information over many edges to arrive at the distribution of travel time over a route is still theoretically challenging. Understanding travel-time distribution can help resolve many fundamental problems in transportation, quantifying travel uncertainty as an example. We develop a novel statistical perspective to specific types of dynamical processes that mimic the behavior of travel time on real-world networks. We show that, under general conditions, travel-time normalized by distance, follows a Gaussian distribution with route-invariant (universal) location and scale parameters. We develop efficient inference methods for such parameters, with which we propose asymptotic universal confidence and prediction intervals of travel time. We further develop our theory to include road-segment level information to construct route-specific location and scale parameter sequences that produce tighter route-specific Gaussian-based prediction intervals. We illustrate our methods with a real-world case study using precollected mobile GPS data, where we show that the route-specific and route-invariant intervals both achieve the 95\% theoretical coverage levels, where the former result in tighter bounds that also outperform competing models.

2020-04-22

(publié)

www.semanticscholar.org

Language Gans Falling Short

Massimo Caccia

Lucas Caccia

William Fedus

Hugo Larochelle

Joelle Pineau

Laurent Charlin

Generating high-quality text with sufficient diversity is essential for a wide range of Natural Language Generation (NLG) tasks. Maximum-Lik… (voir plus)elihood (MLE) models trained with teacher forcing have consistently been reported as weak baselines, where poor performance is attributed to exposure bias (Bengio et al., 2015; Ranzato et al., 2015); at inference time, the model is fed its own prediction instead of a ground-truth token, which can lead to accumulating errors and poor samples. This line of reasoning has led to an outbreak of adversarial based approaches for NLG, on the account that GANs do not suffer from exposure bias. In this work, we make several surprising observations which contradict common beliefs. First, we revisit the canonical evaluation framework for NLG, and point out fundamental flaws with quality-only evaluation: we show that one can outperform such metrics using a simple, well-known temperature parameter to artificially reduce the entropy of the model's conditional distributions. Second, we leverage the control over the quality / diversity trade-off given by this parameter to evaluate models over the whole quality-diversity spectrum and find MLE models constantly outperform the proposed GAN variants over the whole quality-diversity space. Our results have several implications: 1) The impact of exposure bias on sample quality is less severe than previously thought, 2) temperature tuning provides a better quality / diversity trade-off than adversarial training while being easier to train, easier to cross-validate, and less computationally expensive. Code to reproduce the experiments is available at github.com/pclucas14/GansFallingShort

2019-12-31

ICLR (publié)

doi.org

openreview.net

Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning

Massimo Caccia

Pau Rodríguez

Lucas Caccia

Alexandre Lacoste

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.

2019-12-31

NeurIPS (publié)

doi.org

arxiv.org

Synbols: Probing Learning Algorithms with Synthetic Datasets

Alexandre Lacoste

Pau Rodríguez

Frédéric Branchaud-Charron

Parmida Atighehchian

Massimo Caccia

Matt Craddock

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.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (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.

2019-12-31

2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP) (publié)

doi.org

Continual Learning of New Sound Classes Using Generative Replay

Zhepei Wang

Yusuf Cem Sübakan

Efthymios Tzinis

Paris Smaragdis

Laurent Charlin

Continual learning consists in incrementally training a model on a sequence of datasets and testing on the union of all datasets. In this pa… (voir plus)per, we examine continual learning for the problem of sound classification, in which we wish to refine already trained models to learn new sound classes. In practice one does not want to maintain all past training data and retrain from scratch, but naively updating a model with new data(sets) results in a degradation of already learned tasks, which is referred to as "catastrophic forgetting." We develop a generative replay procedure for generating training audio spectrogram data, in place of keeping older training datasets. We show that by incrementally refining a classifier with generative replay a generator that is 4% of the size of all previous training data matches the performance of refining the classifier keeping 20% of all previous training data. We thus conclude that we can extend a trained sound classifier to learn new classes without having to keep previously used datasets.

2019-10-19

2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (publié)

doi.org

arxiv.org

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Maxime Gasse

Didier Chételat

Nicola Ferroni

Laurent Charlin

Andrea Lodi

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural netw… (voir plus)ork model for learning branch-and-bound variable selection policies, which leverages the natural variable-constraint bipartite graph representation of mixed-integer linear programs. We train our model via imitation learning from the strong branching expert rule, and demonstrate on a series of hard problems that our approach produces policies that improve upon state-of-the-art machine-learning methods for branching and generalize to instances significantly larger than seen during training. Moreover, we improve for the first time over expert-designed branching rules implemented in a state-of-the-art solver on large problems. Code for reproducing all the experiments can be found at this https URL.

2019-09-05

NeurIPS.cc/2019/Reproducibility_Challenge (inconnu)

doi.org

openreview.net

Online Continual Learning with Maximally Interfered Retrieval

Lucas Caccia

Massimo Caccia

Tinne Tuytelaars

Continual learning, the setting where a learning agent is faced with a never ending stream of data, continues to be a great challenge for mo… (voir plus)dern machine learning systems. In particular the online or "single-pass through the data" setting has gained attention recently as a natural setting that is difficult to tackle. Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks. These approaches typically rely on randomly selecting samples from the replay memory or from a generative model, which is suboptimal. In this work, we consider a controlled sampling of memories for replay. We retrieve the samples which are most interfered, i.e. whose prediction will be most negatively impacted by the foreseen parameters update. We show a formulation for this sampling criterion in both the generative replay and the experience replay setting, producing consistent gains in performance and greatly reduced forgetting. We release an implementation of our method at this https URL.

2019-08-10

ArXiv (prépublication)

arxiv.org

Session-Based Social Recommendation via Dynamic Graph Attention Networks

Weiping Song

Zhiping Xiao

Yifan Wang

Laurent Charlin

Ming Zhang

Jian Tang

Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their … (voir plus)users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and context-dependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models.

2019-01-29

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (publié)

doi.org

arxiv.org

The Deconfounded Recommender: A Causal Inference Approach to Recommendation

Yixin Wang

Dawen Liang

Laurent Charlin

David Blei

The goal of a recommender system is to show its users items that they will like. In forming its prediction, the recommender system tries to … (voir plus)answer: "what would the rating be if we 'forced' the user to watch the movie?" This is a question about an intervention in the world, a causal question, and so traditional recommender systems are doing causal inference from observational data. This paper develops a causal inference approach to recommendation. Traditional recommenders are likely biased by unobserved confounders, variables that affect both the "treatment assignments" (which movies the users watch) and the "outcomes" (how they rate them). We develop the deconfounded recommender, a strategy to leverage classical recommendation models for causal predictions. The deconfounded recommender uses Poisson factorization on which movies users watched to infer latent confounders in the data; it then augments common recommendation models to correct for potential confounding bias. The deconfounded recommender improves recommendation and it enjoys stable performance against interventions on test sets.

2018-08-19

ArXiv (prépublication)

arxiv.org

Mila Techaide 2026

Propulsion d'entrepreneurs scientifiques

Avantage IA : productivité dans la fonction publique

Laurent Charlin

Biographie

Étudiants actuels

Publications

Mila Techaide 2026

Propulsion d'entrepreneurs scientifiques

Avantage IA : productivité dans la fonction publique

Mots-clés populaires:

Laurent Charlin

Biographie

Étudiants actuels

Publications