Pascal Poupart
homepage:http://www.cs.uwaterloo.ca/~ppoupart
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Description

Pascal Poupart received a Ph.D. degree in Computer Science from the University of Toronto in 2005. Since August 2004, he is an Assistant Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. Poupart's research focuses on the design and analysis of scalable algorithms for sequential decision making under uncertainty (including Bayesian reinforcement learning), with application to assistive technologies in eldercare, spoken dialogue management and information retrieval. He has served on the program committee of several international conferences, including AAMAS (2006, 2007), UAI (2005, 2006, 2007), ICML (2007), AAAI (2005, 2006, 2007), NIPS (2007) and AISTATS (2007).


Lectures:

lecture
flag Analyzing and Escaping Local Optima in Planning as Inference for Partially Observable Domains
as author at  Sessions,
together with: Data & Web Mining Lab (produced by),
2701 views
  lecture
flag Closing the Gap: Improved Bounds on Optimal POMDP Solutions
as author at  21st International Conference on Automated Planning and Scheduling (ICAPS), Freiburg 2011,
3461 views
tutorial
flag Model-based Bayesian RL
as author at  Tutorials,
9532 views
  tutorial
flag Welcome
as author at  Tutorials,
4419 views