Laurent Charlin

Biography

Laurent Charlin is a Canada CIFAR AI Chair at Mila and an associate professor at HEC, the business school affiliated with the University de Montréal. He is also a core member of Mila—Quebec Institute for Artificial Intelligence.

Charlin’s research focuses on developing novel machine learning models to aid in decision-making. Recent work has focused on learning from data that changes over time, and on applications in fields such as recommender systems and optimization.

He has a number of highly cited publications on dialogue systems (chatbots). He co-developed the Toronto Paper Matching System (TPMS), which has been widely used by computer science conferences for matching reviewers to papers. He has also given MOOCs, introductory talks and media interviews to contribute to knowledge transfer and improve AI literacy.

Current Students

Neda Adl

Master's Research - HEC Montréal

Neda@mila.quebec

Anirudh Buvanesh

PhD - Université de Montréal

Co-supervisor :

Aaron Courville

anirudb1102@gmail.com

felix.gauthier@mila.quebec

Félix Gauthier

Master's Research - HEC Montréal

Soraya Ghassemlou

Master's Research - McGill University

soraya.ghassemlou@mila.quebec

Website

nicolas.goulet@mila.quebec

Nicolas Goulet

PhD - HEC Montréal

Principal supervisor :

Tegan Maharaj

shubham.gupta@mila.quebec

Shubham Gupta

PhD - Université Laval

Principal supervisor :

Cem Subakan

Ben Hudson

PhD - Université de Montréal

Co-supervisor :

Emma Frejinger

ben.hudson@mila.quebec

Website

marc.morissette@gmail.com

Marc-Andre Morissette

Master's Research - Université de Montréal

mizu.nishikawa-toomey@mila.quebec

Mizu Nishikawa-Toomey

PhD - Université de Montréal

Co-supervisor :

Dhanya Sridhar

PhD - Concordia University

Principal supervisor :

Mirco Ravanelli

firat.oncel@mila.quebec

Collaborating Alumni - Université de Montréal

ostapeno@mila.quebec

emiliano.penaloza@mila.quebec

Emiliano Penaloza

PhD - Université de Montréal

Website

omar.salemohamed@mila.quebec

Gaurav Sahu

Postdoctorate - HEC Montréal

Co-supervisor :

Chris Pal

gaurav.sahu@mila.quebec

PhD - Université de Montréal

Yipeng Zhang

PhD - Université de Montréal

yipeng.zhang@mila.quebec

Publications

Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control

Tianyu Shi

FranÃ§ois-Xavier Devailly

Denis Larocque

A number of deep reinforcement-learning (RL) approaches propose to control traffic signals. Compared to traditional approaches, RL approache… (see more)s can learn from higher-dimensionality input road and vehicle sensors and better adapt to varying traffic conditions resulting in reduced travel times (in simulation). However, these RL methods require training from massive traffic sensor data. To offset this relative inefficiency, some recent RL methods have the ability to first learn from small-scale networks and then generalize to unseen city-scale networks without additional retraining (zero-shot transfer). In this work, we study the robustness of such methods along two axes. First, sensor failures and GPS occlusions create missing-data challenges and we show that recent methods remain brittle in the face of these missing data. Second, we provide a more systematic study of the generalization ability of RL methods to new networks with different traffic regimes. Again, we identify the limitations of recent approaches. We then propose using a combination of distributional and vanilla reinforcement learning through a policy ensemble. Building upon the state-of-the-art previous model which uses a decentralized approach for large-scale traffic signal control with graph convolutional networks (GCNs), we first learn models using a distributional reinforcement learning (DisRL) approach. In particular, we use implicit quantile networks (IQN) to model the state-action return distribution with quantile regression. For traffic signal control problems, an ensemble of standard RL and DisRL yields superior performance across different scenarios, including different levels of missing sensor data and traffic flow patterns. Furthermore, the learning scheme of the resulting model can improve zero-shot transferability to different road network structures, including both synthetic networks and real-world networks (e.g., Luxembourg, Manhattan). We conduct extensive experiments to compare our approach to multi-agent reinforcement learning and traditional transportation approaches. Results show that the proposed method improves robustness and generalizability in the face of missing data, varying road networks, and traffic flows.

2023-12-31

IEEE Open Journal of Intelligent Transportation Systems (published)

Model-based graph reinforcement learning for inductive traffic signal control

FranÃ§ois-Xavier Devailly

Denis Larocque

Most reinforcement learning methods for adaptive-traffic-signal-control require training from scratch to be applied on any new intersection … (see more)or after any modification to the road network, traffic distribution, or behavioral constraints experienced during training. Considering 1) the massive amount of experience required to train such methods, and 2) that experience must be gathered by interacting in an exploratory fashion with real road-network-users, such a lack of transferability limits experimentation and applicability. Recent approaches enable learning policies that generalize for unseen road-network topologies and traffic distributions, partially tackling this challenge. However, the literature remains divided between the learning of cyclic (the evolution of connectivity at an intersection must respect a cycle) and acyclic (less constrained) policies, and these transferable methods 1) are only compatible with cyclic constraints and 2) do not enable coordination. We introduce a new model-based method, MuJAM, which, on top of enabling explicit coordination at scale for the first time, pushes generalization further by allowing a generalization to the controllers' constraints. In a zero-shot transfer setting involving both road networks and traffic settings never experienced during training, and in a larger transfer experiment involving the control of 3,971 traffic signal controllers in Manhattan, we show that MuJAM, using both cyclic and acyclic constraints, outperforms domain-specific baselines as well as another transferable approach.

2023-12-31

IEEE Open Journal of Intelligent Transportation Systems (published)

Operational Research: Methods and Applications

Fotios Petropoulos

Gilbert Laporte

Emel Aktas

Sibel A. Alumur

Claudia Archetti

Hayriye Ayhan

Maria Battarra

Julia A. Bennell

Jean-Marie Bourjolly

John E. Boylan

Michele Breton

David Canca

Bo Chen

Cihan Tugrul Cicek

Louis Anthony Cox, Jr

Christine S.M. Currie

Erik Demeulemeester

Li Ding

Stephen M. Disney … (see 62 more)

Matthias Ehrgott

Martin J. Eppler

Gunes Erdogan

Bernard Fortz

L. Alberto Franco

Jens Frische

Salvatore Greco

Amanda J. Gregory

Raimo P. Hamalainen

Willy Herroelen

Mike Hewitt

Jan Holmstrom

John N. Hooker

Tugce Isik

Jill Johnes

Bahar Y. Kara

Ozlem Karsu

Katherine Kent

Charlotte Kohler

Martin Kunc

Yong-Hong Kuo

Judit Lienert

Adam N. Letchford

Janny Leung

Dong Li

Haitao Li

Ivana Ljubic

Andrea Lodi

Sebastian Lozano

Virginie Lurkin

Silvano Martello

Ian G. McHale

Gerald Midgley

John D.W. Morecroft

Akshay Mutha

Ceyda Oguz

Sanja Petrovic

Ulrich Pferschy

Harilaos N. Psaraftis

Sam Rose

Lauri Saarinen

Said Salhi

Jing-Sheng Song

Dimitrios Sotiros

Kathryn E. Stecke

Arne K. Strauss

Istenc Tarhan

Clemens Thielen

Paolo Toth

Greet Vanden Berghe

Christos Vasilakis

Vikrant Vaze

Daniele Vigo

Kai Virtanen

Xun Wang

Rafał Weron

Leroy White

Tom Van Woensel

Mike Yearworth

E. Alper Yıldırım

Georges Zaccour

Xuying Zhao

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a … (see more)diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes.

2023-12-31

J. Oper. Res. Soc. (published)

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network

Tristan Deleu

Mizu Nishikawa-Toomey

Jithendaraa Subramanian

Nikolay Malkin

Yoshua Bengio

Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied … (see more)to the problem of inferring the marginal posterior distribution over the directed acyclic graph (DAG) of a Bayesian Network, given a dataset of observations. Based on recent advances extending this framework to non-discrete sample spaces, we propose in this paper to approximate the joint posterior over not only the structure of a Bayesian Network, but also the parameters of its conditional probability distributions. We use a single GFlowNet whose sampling policy follows a two-phase process: the DAG is first generated sequentially one edge at a time, and then the corresponding parameters are picked once the full structure is known. Since the parameters are included in the posterior distribution, this leaves more flexibility for the local probability models of the Bayesian Network, making our approach applicable even to non-linear models parametrized by neural networks. We show that our method, called JSP-GFN, offers an accurate approximation of the joint posterior, while comparing favorably against existing methods on both simulated and real data.

2023-12-11

Conference on Neural Information Processing Systems (Accept (poster))

openreview.net

Predictive inference for travel time on transportation networks

Mohamad Elmasri

Aurélie Labbe

Denis Larocque

2023-11-30

The Annals of Applied Statistics (published)

Challenging Common Assumptions About Catastrophic Forgetting and Knowledge Accumulation

Timothee LESORT

Pau Rodríguez

Md Rifat Arefin

Building learning agents that can progressively learn and accumulate knowledge is the core goal of the continual learning (CL) research fiel… (see more)d. Unfortunately, training a model on new data usually compromises the performance on past data. In the CL literature, this effect is referred to as catastrophic forgetting (CF). CF has been largely studied, and a plethora of methods have been proposed to address it on short sequences of non-overlapping tasks. In such setups, CF always leads to a quick and significant drop in performance in past tasks. Nevertheless, despite CF, recent work showed that SGD training on linear models accumulates knowledge in a CL regression setup. This phenomenon becomes especially visible when tasks reoccur. We might then wonder if DNNs trained with SGD or any standard gradient-based optimization accumulate knowledge in such a way. Such phenomena would have interesting consequences for applying DNNs to real continual scenarios. Indeed, standard gradient-based optimization methods are significantly less computationally expensive than existing CL algorithms. In this paper, we study the progressive knowledge accumulation (KA) in DNNs trained with gradient-based algorithms in long sequences of tasks with data re-occurrence. We propose a new framework, SCoLe (Scaling Continual Learning), to investigate KA and discover that catastrophic forgetting has a limited effect on DNNs trained with SGD. When trained on long sequences with data sparsely re-occurring, the overall accuracy improves, which might be counter-intuitive given the CF phenomenon. We empirically investigate KA in DNNs under various data occurrence frequencies and propose simple and scalable strategies to increase knowledge accumulation in DNNs.

2023-11-19

Conference on Lifelong Learning Agents (published)

Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges

Massimo Caccia

Jonas Mueller

Taesup Kim

Rasool Fakoor

2023-11-19

Proceedings of The 2nd Conference on Lifelong Learning Agents (published)

Should We Feed the Trolls? Using Marketer-Generated Content to Explain Average Toxicity and Product Usage

Marcelo Vinhal Nepomuceno

Hooman Rahemi

Tolga Cenesizoglu

2023-06-28

Journal of Interactive Marketing (published)

From IID to the Independent Mechanisms assumption in continual learning

Oleksiy Ostapenko

Pau Rodríguez

Alexandre Lacoste

2023-06-03

Proceedings of The First AAAI Bridge Program on Continual Causality (published)

Towards Compute-Optimal Transfer Learning

Massimo Caccia

Alexandre Galashov

Arthur Douillard

Amal Rannen-Triki

Dushyant Rao

Michela Paganini

Marc'aurelio Ranzato

Razvan Pascanu

2023-04-24

ArXiv (preprint)

Iorl: Inductive-Offline-Reinforcement-Learning for Traffic Signal Control Warmstarting

FranÃ§ois-Xavier Devailly

Denis Larocque

2022-12-31

Social Science Research Network (published)

Continual Learning with Foundation Models: An Empirical Study of Latent Replay

Oleksiy Ostapenko

Timothee LESORT

Pau Rodríguez

Md Rifat Arefin

Arthur Douillard

Irina Rish

Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a variety of… (see more) downstream tasks and domains. Motivated by this, we study the efficacy of pre-trained vision models as a foundation for downstream continual learning (CL) scenarios. Our goal is twofold. First, we want to understand the compute-accuracy trade-off between CL in the raw-data space and in the latent space of pre-trained encoders. Second, we investigate how the characteristics of the encoder, the pre-training algorithm and data, as well as of the resulting latent space affect CL performance. For this, we compare the efficacy of various pre-trained models in large-scale benchmarking scenarios with a vanilla replay setting applied in the latent and in the raw-data space. Notably, this study shows how transfer, forgetting, task similarity and learning are dependent on the input data characteristics and not necessarily on the CL algorithms. First, we show that under some circumstances reasonable CL performance can readily be achieved with a non-parametric classifier at negligible compute. We then show how models pre-trained on broader data result in better performance for various replay sizes. We explain this with representational similarity and transfer properties of these representations. Finally, we show the effectiveness of self-supervised pre-training for downstream domains that are out-of-distribution as compared to the pre-training domain. We point out and validate several research directions that can further increase the efficacy of latent CL including representation ensembling. The diverse set of datasets used in this study can serve as a compute-efficient playground for further CL research. The codebase is available under https://github.com/oleksost/latent_CL.

2022-11-27

Conference on Lifelong Learning Agents (published)