Automated Hierarchy Discovery for Planning in Partially Observable Environments. - Laurent Charlin; Pascal Poupart; Romy Shioda (NIPS, 2006)

Hierarchical POMDP Controller Optimization by Likelihood Maximization. - Marc Toussaint; Laurent Charlin; Pascal Poupart (UAI, 2008)

A Framework for Optimizing Paper Matching. - Laurent Charlin; Richard S. Zemel; Craig Boutilier (UAI, 2011)

Active Learning for Matching Problems. - Laurent Charlin; Richard S. Zemel; Craig Boutilier (ICML, 2012)

Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. - Daniel Tarlow; Kevin Swersky; Laurent Charlin; Ilya Sutskever; Richard S. Zemel (ICML (3), 2013)

Leveraging user libraries to bootstrap collaborative filtering. - Laurent Charlin; Richard S. Zemel; Hugo Larochelle (KDD, 2014)

Content-based recommendations with Poisson factorization. - Prem Gopalan; Laurent Charlin; David M. Blei (NIPS, 2014)

Deep Exponential Families. - Rajesh Ranganath; Linpeng Tang; Laurent Charlin; David M. Blei (AISTATS, 2015)

Dynamic Poisson Factorization. - Laurent Charlin; Rajesh Ranganath; James McInerney; David M. Blei (RecSys, 2015)

A Survey of Available Corpora for Building Data-Driven Dialogue Systems. - Iulian Vlad Serban; Ryan Lowe; Peter Henderson 002; Laurent Charlin; Joelle Pineau (CoRR, 2015)

Supervised and Active Learning for Recommender Systems. - Laurent Charlin (2015)

How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation. - Chia-Wei Liu; Ryan Lowe; Iulian Serban; Michael Noseworthy; Laurent Charlin; Joelle Pineau (EMNLP, 2016)

Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence. - Dawen Liang; Jaan Altosaar; Laurent Charlin; David M. Blei (RecSys, 2016)

On the Evaluation of Dialogue Systems with Next Utterance Classification. - Ryan Lowe; Iulian Vlad Serban; Michael Noseworthy; Laurent Charlin; Joelle Pineau (SIGDIAL Conference, 2016)

Modeling User Exposure in Recommendation. - Dawen Liang; Laurent Charlin; James McInerney; David M. Blei (WWW, 2016)

Generative Deep Neural Networks for Dialogue: A Short Review. - Iulian Vlad Serban; Ryan Lowe; Laurent Charlin; Joelle Pineau (CoRR, 2016)

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues. - Iulian Vlad Serban; Alessandro Sordoni; Ryan Lowe; Laurent Charlin; Joelle Pineau; Aaron C. Courville; Yoshua Bengio (AAAI, 2017)

A Sparse Probabilistic Model of User Preference Data. - Matthew Smith 014; Laurent Charlin; Joelle Pineau (Canadian AI, 2017)

Learnable Explicit Density for Continuous Latent Space and Variational Inference. - Chin-Wei Huang; Ahmed Touati; Laurent Dinh; Michal Drozdzal; Mohammad Havaei; Laurent Charlin; Aaron C. Courville (CoRR, 2017)

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks. - Nan Rosemary Ke; Anirudh Goyal; Olexa Bilaniuk; Jonathan Binas; Laurent Charlin; Chris Pal; Yoshua Bengio (CoRR, 2017)

Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus. - Ryan Thomas Lowe; Nissan Pow; Iulian Vlad Serban; Laurent Charlin; Chia-Wei Liu; Joelle Pineau (Dialogue Discourse, 2017)

Focused Hierarchical RNNs for Conditional Sequence Processing. - Nan Rosemary Ke; Konrad Zolna; Alessandro Sordoni; Zhouhan Lin; Adam Trischler; Yoshua Bengio; Joelle Pineau; Laurent Charlin; Christopher J. Pal (ICML, 2018)

Towards Deep Conversational Recommendations. - Raymond Li; Samira Ebrahimi Kahou; Hannes Schulz; Vincent Michalski; Laurent Charlin; Chris Pal (NeurIPS, 2018)

The Deconfounded Recommender: A Causal Inference Approach to Recommendation. - Yixin Wang 002; Dawen Liang; Laurent Charlin; David M. Blei (CoRR, 2018)

A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version. - Iulian Vlad Serban; Ryan Lowe; Peter Henderson 002; Laurent Charlin; Joelle Pineau (Dialogue Discourse, 2018)

Online Continual Learning with Maximal Interfered Retrieval. - Rahaf Aljundi; Eugene Belilovsky; Tinne Tuytelaars; Laurent Charlin; Massimo Caccia; Min Lin; Lucas Page-Caccia (NeurIPS, 2019)

Exact Combinatorial Optimization with Graph Convolutional Neural Networks. - Maxime Gasse; Didier Chételat; Nicola Ferroni; Laurent Charlin; Andrea Lodi 001 (NeurIPS, 2019)

Continual Learning of New Sound Classes Using Generative Replay. - Zhepei Wang; Y. Cem Sübakan; Efthymios Tzinis; Paris Smaragdis; Laurent Charlin (WASPAA, 2019)

Session-Based Social Recommendation via Dynamic Graph Attention Networks. - Weiping Song; Zhiping Xiao 001; Yifan Wang 014; Laurent Charlin; Ming Zhang 004; Jian Tang 005 (WSDM, 2019)

Online Continual Learning with Maximally Interfered Retrieval. - Rahaf Aljundi; Lucas Caccia; Eugene Belilovsky; Massimo Caccia; Min Lin; Laurent Charlin; Tinne Tuytelaars (CoRR, 2019)

A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM. - Iulian Vlad Serban; Varun Gupta; Ekaterina Kochmar; Dung Do Vu; Robert Belfer; Joelle Pineau; Aaron C. Courville; Laurent Charlin; Yoshua Bengio (AIED (2), 2020)

Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles. - Yao Lu; Yue Dong 002; Laurent Charlin (EMNLP (1), 2020)

Language GANs Falling Short. - Massimo Caccia; Lucas Caccia; William Fedus; Hugo Larochelle; Joelle Pineau; Laurent Charlin (ICLR, 2020)

On the Effectiveness of Two-Step Learning for Latent-Variable Models. - Y. Cem Sübakan; Maxime Gasse; Laurent Charlin (MLSP, 2020)

Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning. - Massimo Caccia; Pau Rodríguez; Oleksiy Ostapenko; Fabrice Normandin; Min Lin; Lucas Page-Caccia; Issam Hadj Laradji; Irina Rish; Alexandre Lacoste; David Vázquez 001; Laurent Charlin (NeurIPS, 2020)

Synbols: Probing Learning Algorithms with Synthetic Datasets. - Alexandre Lacoste; Pau Rodríguez López; Frederic Branchaud-Charron; Parmida Atighehchian; Massimo Caccia; Issam Hadj Laradji; Alexandre Drouin; Matt Craddock; Laurent Charlin; David Vázquez 001 (NeurIPS, 2020)

Causal Inference for Recommender Systems. - Yixin Wang 002; Dawen Liang; Laurent Charlin; David M. Blei (RecSys, 2020)

Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning. - Massimo Caccia; Pau Rodríguez; Oleksiy Ostapenko; Fabrice Normandin; Min Lin; Lucas Caccia; Issam H. Laradji; Irina Rish; Alexandre Lacoste; David Vázquez 001; Laurent Charlin (CoRR, 2020)

A Comparative Study of Learning Outcomes for Online Learning Platforms. - François St-Hilaire; Nathan Burns; Robert Belfer; Muhammad Shayan; Ariella Smofsky; Dung Do Vu; Antoine Frau; Joseph Potochny; Farid Faraji; Vincent Pavero; Neroli Ko; Ansona Onyi Ching; et al. (AIED (2), 2021)

Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations. - Pau Rodríguez; Massimo Caccia; Alexandre Lacoste; Lee Zamparo; Issam H. Laradji; Laurent Charlin; David Vázquez 001 (ICCV, 2021)

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. - Maxime Gasse; Simon Bowly; Quentin Cappart; Jonas Charfreitag; Laurent Charlin; Didier Chételat; Antonia Chmiela; Justin Dumouchelle; Ambros M. Gleixner; Aleksandr M. Kazachkov; Elias B. Khalil; Pawel Lichocki; et al. (NeurIPS (Competition and Demos), 2021)

Continual Learning via Local Module Composition. - Oleksiy Ostapenko; Pau Rodríguez; Massimo Caccia; Laurent Charlin (NeurIPS, 2021)

Pretraining Representations for Data-Efficient Reinforcement Learning. - Max Schwarzer; Nitarshan Rajkumar; Michael Noukhovitch; Ankesh Anand; Laurent Charlin; R. Devon Hjelm; Philip Bachman; Aaron C. Courville (NeurIPS, 2021)

Sequoia: A Software Framework to Unify Continual Learning Research. - Fabrice Normandin; Florian Golemo; Oleksiy Ostapenko; Pau Rodríguez; Matthew D. Riemer; Julio Hurtado; Khimya Khetarpal; Dominic Zhao; Ryan Lindeborg; Timothée Lesort; Laurent Charlin; Irina Rish; et al. (CoRR, 2021)

Continual Learning with Foundation Models: An Empirical Study of Latent Replay. - Oleksiy Ostapenko; Timothée Lesort; Pau Rodríguez; Md Rifat Arefin; Arthur Douillard; Irina Rish; Laurent Charlin (CoLLAs, 2022)

Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning. - Max B. Paulus; Giulia Zarpellon; Andreas Krause 001; Laurent Charlin; Chris J. Maddison (ICML, 2022)

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. - Maxime Gasse; Quentin Cappart; Jonas Charfreitag; Laurent Charlin; Didier Chételat; Antonia Chmiela; Justin Dumouchelle; Ambros M. Gleixner; Aleksandr M. Kazachkov; Elias B. Khalil; Pawel Lichocki; Andrea Lodi 001; et al. (CoRR, 2022)

A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions. - François St-Hilaire; Dung Do Vu; Antoine Frau; Nathan Burns; Farid Faraji; Joseph Potochny; Stephane Robert; Arnaud Roussel; Selene Zheng; Taylor Glazier; Junfel Vincent Romano; Robert Belfer; et al. (CoRR, 2022)

Foundational Models for Continual Learning: An Empirical Study of Latent Replay. - Oleksiy Ostapenko; Timothée Lesort; Pau Rodríguez; Md Rifat Arefin; Arthur Douillard; Irina Rish; Laurent Charlin (CoRR, 2022)

Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline. - Massimo Caccia; Jonas Mueller 001; Taesup Kim; Laurent Charlin; Rasool Fakoor (CoRR, 2022)

Scaling the Number of Tasks in Continual Learning. - Timothée Lesort; Oleksiy Ostapenko; Diganta Misra; Md Rifat Arefin; Pau Rodríguez; Laurent Charlin; Irina Rish (CoRR, 2022)

Model-based graph reinforcement learning for inductive traffic signal control. - François-Xavier Devailly; Denis Larocque; Laurent Charlin (CoRR, 2022)

Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes. - Mizu Nishikawa-Toomey; Tristan Deleu; Jithendaraa Subramanian; Yoshua Bengio; Laurent Charlin (CoRR, 2022)

IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control. - François-Xavier Devailly; Denis Larocque; Laurent Charlin (IEEE Trans. Intell. Transp. Syst., 2022)

From IID to the Independent Mechanisms assumption in continual learning. - Oleksiy Ostapenko; Pau Rodríguez; Alexandre Lacoste; Laurent Charlin (AAAI Bridge Program, 2023)

Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges. - Massimo Caccia; Jonas Mueller 001; Taesup Kim; Laurent Charlin; Rasool Fakoor (CoLLAs, 2023)

Challenging Common Assumptions about Catastrophic Forgetting and Knowledge Accumulation. - Timothée Lesort; Oleksiy Ostapenko; Pau Rodríguez; Diganta Misra; Md Rifat Arefin; Laurent Charlin; Irina Rish (CoLLAs, 2023)

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network. - Tristan Deleu; Mizu Nishikawa-Toomey; Jithendaraa Subramanian; Nikolay Malkin; Laurent Charlin; Yoshua Bengio (NeurIPS, 2023)

Towards Compute-Optimal Transfer Learning. - Massimo Caccia; Alexandre Galashov; Arthur Douillard; Amal Rannen-Triki; Dushyant Rao; Michela Paganini; Laurent Charlin; Marc'Aurelio Ranzato; Razvan Pascanu (CoRR, 2023)

Improving the generalizability and robustness of large-scale traffic signal control. - Tianyu Shi; François-Xavier Devailly; Denis Larocque; Laurent Charlin (CoRR, 2023)

Integrating Present and Past in Unsupervised Continual Learning. - Yipeng Zhang; Laurent Charlin; Richard S. Zemel; Mengye Ren (CoLLAs, 2024)

Towards Modular LLMs by Building and Reusing a Library of LoRAs. - Oleksiy Ostapenko; Zhan Su 002; Edoardo M. Ponti; Laurent Charlin; Nicolas Le Roux; Lucas Caccia; Alessandro Sordoni (ICML, 2024)

LitLLM: A Toolkit for Scientific Literature Review. - Shubham Agarwal; Issam H. Laradji; Laurent Charlin; Christopher Pal (CoRR, 2024)

Towards Modular LLMs by Building and Reusing a Library of LoRAs. - Oleksiy Ostapenko; Zhan Su 002; Edoardo Maria Ponti; Laurent Charlin; Nicolas Le Roux; Matheus Pereira; Lucas Caccia; Alessandro Sordoni (CoRR, 2024)

TEARS: Textual Representations for Scrutable Recommendations. - Emiliano Penaloza; Olivier Gouvert; Haolun Wu; Laurent Charlin (CoRR, 2024)

Discovering Data Structures: Nearest Neighbor Search and Beyond. - Omar Salemohamed; Laurent Charlin; Shivam Garg 001; Vatsal Sharan; Gregory Valiant (CoRR, 2024)

LLMs for Literature Review: Are we there yet? - Shubham Agarwal; Gaurav Sahu; Abhay Puri; Issam H. Laradji; Krishnamurthy Dj Dvijotham; Jason Stanley; Laurent Charlin; Christopher Pal (CoRR, 2024)

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; Michèle Breton; David Canca; et al. (J. Oper. Res. Soc., 2024)

TEARS: Text Representations for Scrutable Recommendations. - Emiliano Penaloza; Olivier Gouvert; Haolun Wu; Laurent Charlin (WWW, 2025)

LitLLMs, LLMs for Literature Review: Are we there yet? - Shubham Agarwal; Gaurav Sahu; Abhay Puri; Issam H. Laradji; Krishnamurthy Dj Dvijotham; Jason Stanley; Laurent Charlin; Christopher Pal (Trans. Mach. Learn. Res., 2025)

Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization. - Emiliano Penaloza; Tianyue H. Zhan; Laurent Charlin; Mateo Espinosa Zarlenga (CoRR, 2025)

How to Train Your LLM Web Agent: A Statistical Diagnosis. - Dheeraj Vattikonda; Santhoshi Ravichandran; Emiliano Penaloza; Hadi Nekoei; Megh Thakkar; Thibault Le Sellier de Chezelles; Nicolas Gontier; Miguel Muñoz-Mármol; Sahar Omidi Shayegan; Stefania Raimondo; Xue Liu; Alexandre Drouin; et al. (CoRR, 2025)

Audio Prototypical Network For Controllable Music Recommendation. - Firat Öncel; Emiliano Penaloza; Haolun Wu; Shubham Gupta; Mirco Ravanelli; Laurent Charlin; Cem Subakan (CoRR, 2025)