Marek Petrik

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Mohammad GhavamzadehAmazonVerified email at amazon.com
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
Chin Pang HoCity University of Hong KongVerified email at cityu.edu.hk
Bahram BehzadianMetaVerified email at meta.com
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 LiuPhD, AAAI SM, IEEE SMVerified email at cs.umass.edu
Wolfram WiesemannProfessor of Analytics and Operations, Imperial College Business SchoolVerified email at imperial.ac.uk
Daniel S. BrownAssistant Professor, Robotics Center and Kahlert School of Computing, University of UtahVerified email at cs.utah.edu
Yinlam ChowResearch Scientist, Google ResearchVerified email at google.com
Amit DhurandharPrincipal Research Scientist, IBMVerified email at us.ibm.com
Elita LoboUniversity of Massachusetts AmherstVerified email at cs.umass.edu
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
Prateek JainThe TradedeskVerified email at prateekjain.name
Gavin TaylorUS Naval AcademyVerified email at usna.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	187	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	178	2020
Safe policy improvement by minimizing robust baseline regret M Ghavamzadeh, M Petrik, Y Chow Advances in Neural Information Processing Systems 29, 2016	155	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	92	2010
An Analysis of Laplacian Methods for Value Function Approximation in MDPs. M Petrik IJCAI, 2574-2579, 2007	91	2007
Biasing approximate dynamic programming with a lower discount factor M Petrik, B Scherrer Advances in neural information processing systems 21, 2008	71	2008
Beyond confidence regions: Tight bayesian ambiguity sets for robust mdps M Petrik, RH Russel Advances in neural information processing systems 32, 2019	69	2019
Fast Bellman updates for robust MDPs CP Ho, M Petrik, W Wiesemann International Conference on Machine Learning, 1979-1988, 2018	69	2018
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	62	2017
Learning parallel portfolios of algorithms M Petrik, S Zilberstein Annals of Mathematics and Artificial Intelligence 48, 85-106, 2006	61	2006
Partial policy iteration for l1-robust markov decision processes CP Ho, M Petrik, W Wiesemann Journal of Machine Learning Research 22 (275), 1-46, 2021	56	2021
Constraint relaxation in approximate linear programs M Petrik, S Zilberstein Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	47	2009
Tight approximations of dynamic risk measures DA Iancu, M Petrik, D Subramanian Mathematics of Operations Research 40 (3), 655-682, 2015	46	2015
A bilinear programming approach for multiagent planning M Petrik, S Zilberstein Journal of Artificial Intelligence Research 35, 235-274, 2009	45	2009
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
Bayesian robust optimization for imitation learning D Brown, S Niekum, M Petrik Advances in Neural Information Processing Systems 33, 2479-2491, 2020	40	2020
Average-Reward Decentralized Markov Decision Processes. M Petrik, S Zilberstein IJCAI, 1997-2002, 2007	38	2007
Social media and customer behavior analytics for personalized customer engagements S Buckley, M Ettl, P Jain, R Luss, M Petrik, RK Ravi, C Venkatramani IBM Journal of Research and Development 58 (5/6), 7: 1-7: 12, 2014	35	2014
Proximal Gradient Temporal Difference Learning Algorithms. B Liu, J Liu, M Ghavamzadeh, S Mahadevan, M Petrik IJCAI, 4195-4199, 2016	32	2016
Anytime coordination using separable bilinear programs M Petrik, S Zilberstein PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE 22 (1), 750, 2007	32	2007

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