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Marek Petrik
Marek Petrik
Verified email at cs.unh.edu - Homepage
Title
Cited by
Cited by
Year
Finite-sample analysis of proximal gradient td algorithms
B Liu, J Liu, M Ghavamzadeh, S Mahadevan, M Petrik
arXiv preprint arXiv:2006.14364, 2020
1392020
Safe policy improvement by minimizing robust baseline regret
M Ghavamzadeh, M Petrik, Y Chow
Advances in Neural Information Processing Systems 29, 2016
1082016
Feature selection using regularization in approximate linear programs for Markov decision processes
M Petrik, G Taylor, R Parr, S Zilberstein
arXiv preprint arXiv:1005.1860, 2010
872010
An Analysis of Laplacian Methods for Value Function Approximation in MDPs.
M Petrik
IJCAI, 2574-2579, 2007
792007
Learning parallel portfolios of algorithms
M Petrik, S Zilberstein
Annals of Mathematics and Artificial Intelligence 48 (1), 85-106, 2006
622006
Biasing approximate dynamic programming with a lower discount factor
M Petrik, B Scherrer
Advances in neural information processing systems 21, 2008
482008
A bilinear programming approach for multiagent planning
M Petrik, S Zilberstein
Journal of Artificial Intelligence Research 35, 235-274, 2009
442009
A practical method for solving contextual bandit problems using decision trees
AN Elmachtoub, R McNellis, S Oh, M Petrik
arXiv preprint arXiv:1706.04687, 2017
432017
Average-Reward Decentralized Markov Decision Processes.
M Petrik, S Zilberstein
IJCAI, 1997-2002, 2007
382007
Tight approximations of dynamic risk measures
DA Iancu, M Petrik, D Subramanian
Mathematics of Operations Research 40 (3), 655-682, 2015
362015
Constraint relaxation in approximate linear programs
M Petrik, S Zilberstein
Proceedings of the 26th Annual International Conference on Machine Learning …, 2009
342009
Anytime coordination using separable bilinear programs
M Petrik, S Zilberstein
PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE 22 (1), 750, 2007
322007
RAAM: The benefits of robustness in approximating aggregated MDPs in reinforcement learning
M Petrik, D Subramanian
Advances in Neural Information Processing Systems 27, 2014
312014
Beyond confidence regions: Tight bayesian ambiguity sets for robust mdps
M Petrik, RH Russel
Advances in neural information processing systems 32, 2019
292019
Fast Bellman updates for robust MDPs
CP Ho, M Petrik, W Wiesemann
International Conference on Machine Learning, 1979-1988, 2018
292018
An approximate solution method for large risk-averse Markov decision processes
M Petrik, D Subramanian
arXiv preprint arXiv:1210.4901, 2012
282012
Robust Approximate Bilinear Programming for Value Function Approximation.
M Petrik, S Zilberstein
Journal of Machine Learning Research 12 (10), 2011
282011
Proximal Gradient Temporal Difference Learning Algorithms.
B Liu, J Liu, M Ghavamzadeh, S Mahadevan, M Petrik
IJCAI, 4195-4199, 2016
202016
Agile logistics simulation and optimization for managing disaster responses
F Barahona, M Ettl, M Petrik, PM Rimshnick
2013 Winter Simulations Conference (WSC), 3340-3351, 2013
202013
Hybrid least-squares algorithms for approximate policy evaluation
J Johns, M Petrik, S Mahadevan
Machine learning 76 (2), 243-256, 2009
202009
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