laurent

Laurent Charlin

Email: lcharlin [at] gmail [dot] com
[PGP key]

[bio]  [papers]  [talks]  [projects]  [press]  [CV]

I am interested in developping novel machine learning models, particularly probabilistic graphical models, to help in decision making. My recent work has focussed 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 analyze different data.


Short Bio:

[Curent] Postdoc at McGill University. Advised by Joelle Pineau.

Postdoc at Princeton University and Columbia University. Advised by David Blei.

PhD in the machine learning group at the University of Toronto. Advised by Craig Boutilier and Rich Zemel.

Master's at the University of Toronto. Advised by Pascal Poupart.


Papers:

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:

  • 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

Press:

  • 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

Selected Talks:

  • Recommender Systems: going beyond Matrix Factorization, Bloomberg, New York - 03/2015.
  • Recommender Systems: going beyond Matrix Factorization, McGill University - 02/2015.
  • 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.
Last updated: August 2015