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About me: I'm a PhD student in the machine learning group at the University of Toronto. I'm co-advised by Craig Boutilier and Rich Zemel.

My research interests can be broadly defined as spanning the field of machine learning. More precisely, my current work is on extending the capabilities of recommender systems.

I have also done work in reasoning/decision-making under uncertainty (see below) and I'm generally interested in applying learning methods to different problems as well as in learning theory.

Before coming to Toronto I finished a Master's at the University of Waterloo (Canada) in the AI group of the CS school, in December 2006. My work, under the supervision of Pascal Poupart, focused on discovering abstractions in planning problems modelled as partially observable Markov decision processes (POMDPs). For more information, look at our NIPS '07 paper (or at my Master's thesis).

• 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.
  [pdf]

• Active Learning for Matching Problems
  Laurent Charlin, Richard Zemel, Craig Boutilier
  In proceedings of the International Conference on Machine Learning (ICML), 2012.
  [pdf] [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.
  [pdf] [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.
  [pdf] [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)
  [pdf]

• 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.
  [ps] [ps.gz] [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.
  [pdf]

• 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]

• Rich Zemel and I have developed a system to help match reviewers to papers for conferences. The system is now used by several of the leading machine learning and computer vision conferences.
  For more information check out: Toronto Paper Matching System

• Our research about active learning for matching problems, and especially its links to online dating, was briefly featured in the UofT Magazine:
  Computer Says No

• Seminar in the intelligent-environments group (CREI), Université de Sherbrooke - 02/2013.
• AdaComp seminar, National University of Singapore (NUS) - Hierarchical POMDP Controller Optimization by Likelihood Maximization, 11/2008.
• Reinforcement Learning seminar, McGill University - Hierarchical POMDP Controller Optimization by Likelihood Maximization, 11/2008.
• Machine Learning Seminar, TU-Berlin, Germany - Automated Hierarchy Discovery for Planning in Partially Observable Environments, 07/2007.
• MITACS Machine Learning Seminar, McGill University - Automated Hierarchy Discovery for Planning in Partially Observable Environments, 05/2007.
• CoGS Seminars, University of Toronto, Automated Hierarchy Discovery for Planning in Partially Observable Environments, 02/2007.
• AI seminar, University of Waterloo - Automated Hierarchy Discovery for Planning in Partially, 01/2007.