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Laurent Charlin, Ph.D.
Associate professor at HEC Montréal
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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:
Papers:
PreprintsOperational Research: Methods and ApplicationsEncyclopedic article [arXiv] Model-based graph reinforcement learning for inductive traffic signal control FX Devailly, D Larocque, L Charlin [arXiv] PublicationsInference for travel time on transportation networksMohamad 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] IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control François-Xavier 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 Data-Efficient 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 Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vázquez In the proceedings of NeurIPS 2020 [arXiv] Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles Yao Lu, Yue Dong, Laurent Charlin In the proceedings of EMNLP 2020 (Short Paper) [paper] [Dataset] On the effectiveness of two-step learning for latent-variable 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] Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau. (Journal) Dialogue and Discourse, 2018 [paper] Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus Ryan Lowe, Nissan Pow, Iulian Vlad Serban, Laurent Charlin, Chia-Wei Liu, Joelle Pineau (Journal) Dialogue and Discourse, 2017 [paper] A Hierarchical Latent Variable Encoder-Decoder 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 Chia-Wei 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 Co-occurrence 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] Content-based 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 paper-reviewer assignment system Laurent Charlin, Richard Zemel. In the ICML Workshop on Peer Reviewing and Publishing Models (PEER), 2013. [paper] Stochastic k-Neighborhood 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 runner-up 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, CS-2006-47, 2006. [paper pdf]
Master's Thesis, School of Computer Science, University of Waterloo, December 2006. [ps] [ps.gz] [pdf] [bibtex] Projects:
For more information check out: Toronto Paper Matching System (TPMS) Courses:
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