My Interests:
I develop novel machine learning models to help in decision making. My recent
work has focussed on both methods (notably continual learning) as well
as in applications in fields such as recommender systems, dialog
systems, and optimization.
I am generally interested in applying learning methods to analyze different data.
Short Bio:
[Current] Associate Professor at HEC Montréal (U. Montreal's business school).
Assistant Professor at HEC Montréal.
Postdoc at McGill University. Advised by Joelle Pineau.
Postdoc at Princeton University and Columbia University. Advised by David Blei.
Ph.D. in the machine learning group at the University of Toronto.
Advised by Richard Zemel and Craig Boutilier.
Master's at the University of Waterloo. Advised by Pascal Poupart.
Research Group:
Alumni:
 Behrouz Babaki (Data Scientist, Wise Systems)
 FrançoisXavier Devailly (Principal Machine Learning Scientist, Desjardins)
 Mohamad Elmasri (Postdoc, U.Toronto)
 Maxime Gasse (Senior Research Scientist, ServiceNow)
 Yao Lu (PhD student, UCL)
 Nitarshan Rajkumar (PhD student, Cambridge U.)
 Cem Sübakan (Assistant Professor, U. Laval)
 ChinWei Huang (Senior Researcher, Microsoft Research Amsterdam)
 Milad KeshvariFard (Associate Professor, University of Bath)
 Amir Raza
 Tianyu Shi (PhD student, U. Toronto)
 Nicholas Vachon
Papers:
Preprints
 Operational Research: Methods and Applications
Encyclopedic article
[arXiv]
 Modelbased graph reinforcement learning for inductive
traffic signal control
FX Devailly, D Larocque, L Charlin
[arXiv]
 Inference for travel time on transportation networks
Mohamad Elmasri, Aurélie Labbe, Denis Larocque, Laurent Charlin
Annals of Applied Statistics, Accepted 2023
[arXiv]
 Continual Learning with Foundation Models: An Empirical Study of Latent Replay.
Oleksiy Ostapenko, Timothee LESORT, Pau Rodriguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin
In the proceedings of CoLLAs 2022
[arXiv]
 Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning
Max Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris Maddison
In the proceedings of ICML 2022
[PDF] [ICML Video]
 IGRL: Inductive Graph Reinforcement Learning for MassiveScale Traffic Signal Control
FrançoisXavier Devailly, Denis Larocque, Laurent Charlin
IEEE Transactions on Intelligent Transportation Systems 2022
[arXiv]
 Continual Learning via Local Module Composition
Oleksiy Ostapenko, Pau Rodríguez, Massimo Caccia, Laurent Charlin
In the proceedings of NeurIPS 2021
[arXiv]
 Pretraining Representations for DataEfficient Reinforcement
Learning
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand,
Laurent Charlin, Devon Hjelm, Philip Bachman, Aaron Courville
In the proceedings of NeurIPS 2021
[arXiv]
 Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin,
Min Lin, Lucas Caccia, Issam Laradji, Irina Rish, Alexande Lacoste,
David Vazquez, Laurent Charlin
In the proceedings of NeurIPS 2020
[arXiv]
 Synbols: Probing Learning Algorithms with Synthetic Datasets
Alexandre Lacoste, Pau Rodríguez, Frédéric
BranchaudCharron, Parmida Atighehchian, Massimo Caccia, Issam
Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David
Vázquez
In the proceedings of NeurIPS 2020
[arXiv]
 MultiXScience: A Largescale Dataset for Extreme Multidocument Summarization of Scientific Articles
Yao Lu, Yue Dong, Laurent Charlin
In the proceedings of EMNLP 2020 (Short Paper)
[paper] [Dataset]
 On the effectiveness of twostep learning for latentvariable models
Cem Subakan, Maxime Gasse, Laurent Charlin
In the proceedings of MLSP 2020
[paper]
 Causal Inference for Recommender Systems
Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei
In the proceedings of ACM RecSys 2020 (Short Paper)
[paper] [arXiv (longer version)]
 Language GANs Falling Short
Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joëlle Pineau, Laurent Charlin
In the proceedings of ICLR 2020
[arXiv]
 Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
In the proceedings of NeurIPS 2019
[arXiv] [code]
 Online Continual Learning with Maximal Interfered Retrieval
Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Min
Lin, Laurent Charlin, Tinne Tuytelaars
In the proceedings of NeurIPS 2019
[arXiv]
 Continual Learning of New Sound Classes using Generative Replay
Zhepei Wang, Cem Subakan, Efthymios Tzinis, Paris Smaragdis, Laurent Charlin
In the Proceedings of WASPAA 2019
[arXiv]
 Sessionbased Social Recommendation via Dynamic Graph Attention Networks
Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang and Jian Tang
In the proceedings of WSDM 2019.
[paper]
 Towards Deep Conversational Recommendations
Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski,
Laurent Charlin, Christopher Pal
In the proceedings of NeurIPS 2018
[paper]
[project website (including dataset)]
 Focused Hierarchical RNNs for Conditional Sequence Processing
Nan Rosemary Ke, Konrad Żołna, Alessandro Sordoni, Zhouhan Lin,
Adam Trischler, Yoshua Bengio, Joelle Pineau,
Laurent Charlin, Chris Pal
In the proceedings of ICML 2018
[paper]
 A Survey of Available Corpora For Building DataDriven Dialogue
Systems: The Journal Version
Iulian Vlad Serban,
Ryan Lowe, Peter Henderson, Laurent Charlin,
Joelle Pineau.
(Journal) Dialogue and Discourse, 2018
[paper]
 Training EndtoEnd Dialogue Systems with the Ubuntu Dialogue
Corpus
Ryan Lowe,
Nissan Pow,
Iulian Vlad Serban,
Laurent Charlin,
ChiaWei Liu,
Joelle Pineau
(Journal) Dialogue and Discourse, 2017
[paper]
 A Hierarchical Latent Variable EncoderDecoder Model for Generating Dialogues
Iulian Vlad Serban,
Alessandro Sordoni,
Ryan Lowe,
Laurent Charlin,
Joelle Pineau,
Aaron Courville,
Yoshua Bengio
In the proceedings of AAAI 2017
[paper]
[code]
 How NOT To Evaluate Your Dialogue System: An Empirical Study of
Unsupervised Evaluation Metrics for Dialogue Response Generation
ChiaWei Liu, Ryan Lowe, Iulian Vlad
Serban, Michael Noseworthy,
Laurent Charlin,
Joelle Pineau.
In the proceedings of EMNLP 2016
[arXiv]
 Factorization Meets the Item Embedding: Matrix Factorization
with Item Cooccurrence
Dawen Liang, Jaan Altosaar, Laurent Charlin,
David M. Blei.
In the proceedings of Recsys 2016
[pdf] [code]
 Modeling User Exposure in Recommendation
Dawen Liang, Laurent Charlin,
James McInerney, David M. Blei.
In the proceedings of WWW 2016
[paper]
[code]
 Dynamic Poisson Factorization
Laurent Charlin, Rajesh Ranganath,
James McInerney, David M. Blei.
In the proceedings of RecSys 2015
[paper]
[publication source]
[code]
[slides]
 Deep Exponential Families
Rajesh Ranganath,
Linpeng
Tang, Laurent Charlin, David M. Blei.
In the proceedings of AISTATS 2015
[paper]
[appendix]
[code]
[publication source]
 Contentbased recommendations with Poisson
factorization
Prem
Gopalan, Laurent Charlin, David M. Blei.
In the proceedings of NIPS 2014.
[paper]
[appendix]
[code]
[publication source]
 Leveraging User Libraries to Bootstrap Collaborative
Filtering
Laurent Charlin, Richard Zemel, Hugo Larochelle.
In the proceedings of KDD 2014.
[paper][video lecture]
 The Toronto Paper Matching System: An automated
paperreviewer assignment system
Laurent Charlin,
Richard Zemel.
In the ICML Workshop on Peer Reviewing and Publishing Models
(PEER), 2013. [paper]
 Stochastic kNeighborhood Selection for Supervised and
Unsupervised Learning
Daniel Tarlow,
Kevin Swersky,
Laurent Charlin,
Ilya Sutskever,
Richard Zemel.
In proceeding of the International Conference on
Machine Learning (ICML), 2013. [paper]
[code]
 Active Learning for Matching Problems
Laurent Charlin, Richard Zemel, Craig Boutilier
In proceedings of the International Conference on Machine
Learning (ICML), 2012. [paper]
[bibtex]
[poster]
[short video lecture]
 A Framework for Optimizing Paper Matching
Laurent Charlin, Richard Zemel, Craig Boutilier
In proceedings of Uncertainty in Artificial Intelligence (UAI),
Barcelona, 2011. [paper]
[bibtex] [poster]
 Hierarchical POMDP Controller Optimization by Likelihood
Maximization  Best paper award runnerup
Marc Toussaint, Laurent Charlin, Pascal Poupart
In proceedings of Uncertainty in Artificial Intelligence (UAI),
Helsinki,
2008. [paper]
[bibtex] [video
lecture]
 Hierarchical POMDP Controller Optimization by Likelihood
Maximization
Marc Toussaint, Laurent Charlin, Pascal Poupart
In proceedings of the AAAI workshop on Advancements in POMDP Solvers,
Chicago, 2008. (A longer version has also appeared in UAI'08, see above)
[paper]
 Automated Hierarchy Discovery for Planning in Partially Observable
Environments
Laurent Charlin, Pascal Poupart and Romy Shioda
Advances in Neural Information Processing Systems 19 (NIPS), 2007.
[paper ps]
[paper ps.gz]
[paper pdf]
[bibtex]
 MAXSM: A MultiHeuristic Approach to XML Schema Matching
Mirza Beg, Laurent Charlin and Joel So
University of Waterloo Technical Report, CS200647, 2006.
[paper pdf]
Master's Thesis:
 Automated Hierarchy Discovery for Planning in Partially Observable
Domains,
Master's Thesis, School of Computer Science, University of
Waterloo, December 2006.
[ps]
[ps.gz]
[pdf]
[bibtex]
Projects:
Courses:
