Marek Petrik

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Shlomo ZilbersteinProfessor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
Dharmashankar SubramanianPrincipal Research Staff Member/Manager, IBM ResearchVerified email at us.ibm.com
Reazul Hasan RusselResearch Scientist at MetaVerified email at wildcats.unh.edu
Sridhar MahadevanDirector, Data Science Lab, Adobe Research & Professor, University of Massachusetts, AmherstVerified email at cs.umass.edu
Sechan OhMoloco, Previously at IBM, StanfordVerified email at molocoads.com
Ji LiuMetaVerified email at meta.com
Bo LiuEx-Associate Professor, AAAI SM, IEEE SMVerified email at cs.umass.edu
Amit DhurandharPrincipal Research Scientist, IBMVerified email at us.ibm.com
Adam N. ElmachtoubColumbia University, Dept. of Industrial Engineering and Operations ResearchVerified email at ieor.columbia.edu
Wheeler RumlUniversity of New HampshireVerified email at cs.unh.edu
Dan A. IancuAssociate Professor of Operations, Information and Technology, Stanford UniversityVerified email at stanford.edu
Gavin TaylorUS Naval AcademyVerified email at usna.edu
Ronald ParrProfessor of Computer Science, Duke UniversityVerified email at cs.duke.edu
Prateek JainThe TradedeskVerified email at prateekjain.name
Janusz MareckiDeepMindVerified email at google.com
Francisco BarahonaIBM ResearchVerified email at us.ibm.com
Jeff JohnsGoogleVerified email at google.com
Marco LaumannsVerified email at ethz.ch
Xiaojian WuFacebookVerified email at cs.umass.edu
Stephen BeckerAssociate Professor at University of ColoradoVerified email at colorado.edu

Marek Petrik

University of New Hampshire

Verified email at cs.unh.edu - Homepage

Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
An approximate solution method for large risk-averse Markov decision processes M Petrik, D Subramanian arXiv preprint arXiv:1210.4901, 2012	185	2012
Finite-sample analysis of proximal gradient td algorithms B Liu, J Liu, M Ghavamzadeh, S Mahadevan, M Petrik arXiv preprint arXiv:2006.14364, 2020	165	2020
Safe policy improvement by minimizing robust baseline regret M Ghavamzadeh, M Petrik, Y Chow Advances in Neural Information Processing Systems 29, 2016	138	2016
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	90	2010
An Analysis of Laplacian Methods for Value Function Approximation in MDPs. M Petrik IJCAI, 2574-2579, 2007	89	2007
Biasing approximate dynamic programming with a lower discount factor M Petrik, B Scherrer Advances in neural information processing systems 21, 2008	67	2008
Learning parallel portfolios of algorithms M Petrik, S Zilberstein Annals of Mathematics and Artificial Intelligence 48, 85-106, 2006	61	2006
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	55	2017
Beyond confidence regions: Tight Bayesian ambiguity sets for robust MDPs M Petrik, RH Russel Advances in neural information processing systems 32, 2019	54	2019
Fast Bellman updates for robust MDPs CP Ho, M Petrik, W Wiesemann International Conference on Machine Learning, 1979-1988, 2018	54	2018
A bilinear programming approach for multiagent planning M Petrik, S Zilberstein Journal of Artificial Intelligence Research 35, 235-274, 2009	46	2009
Constraint relaxation in approximate linear programs M Petrik, S Zilberstein Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	45	2009
Tight approximations of dynamic risk measures DA Iancu, M Petrik, D Subramanian Mathematics of Operations Research 40 (3), 655-682, 2015	44	2015
RAAM: The benefits of robustness in approximating aggregated MDPs in reinforcement learning M Petrik, D Subramanian Advances in Neural Information Processing Systems 27, 2014	44	2014
Partial policy iteration for L₁-robust Markov decision processes CP Ho, M Petrik, W Wiesemann The Journal of Machine Learning Research 22 (1), 12612-12657, 2021	39	2021
Average-Reward Decentralized Markov Decision Processes. M Petrik, S Zilberstein IJCAI, 1997-2002, 2007	39	2007
Bayesian robust optimization for imitation learning D Brown, S Niekum, M Petrik Advances in Neural Information Processing Systems 33, 2479-2491, 2020	34	2020
Anytime coordination using separable bilinear programs M Petrik, S Zilberstein PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE 22 (1), 750, 2007	33	2007
Proximal Gradient Temporal Difference Learning Algorithms. B Liu, J Liu, M Ghavamzadeh, S Mahadevan, M Petrik IJCAI, 4195-4199, 2016	28	2016
Robust Approximate Bilinear Programming for Value Function Approximation. M Petrik, S Zilberstein Journal of Machine Learning Research 12 (10), 2011	26	2011

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