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
Sridhar MahadevanDirector, Data Science Lab, Adobe Research & Professor, University of Massachusetts, AmherstVerified email at cs.umass.edu
Bo LiuAuburn UniversityVerified email at cs.umass.edu
Ji LiuUniversity of RochesterVerified email at cs.rochester.edu
Sechan OhMoloco, Previously at IBM, StanfordVerified email at molocoads.com
Reazul Hasan RusselStudent at Reinforcement Learning & Robustness Lab, University of New HampshireVerified email at wildcats.unh.edu
Adam N. ElmachtoubColumbia University, Dept. of Industrial Engineering and Operations ResearchVerified email at ieor.columbia.edu
Amit DhurandharRSM, IBMVerified email at us.ibm.com
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
Wheeler RumlUniversity of New HampshireVerified email at cs.unh.edu
Janusz MareckiDeepMindVerified email at google.com
Francisco BarahonaIBM ResearchVerified email at us.ibm.com
Jeff JohnsFireEyeVerified email at fireeye.com
Xiaojian WuFacebook AIVerified email at cs.umass.edu
Marco LaumannsVerified email at ethz.ch
Stephen BeckerAssistant Professor at University of ColoradoVerified email at colorado.edu
PrateekAppZen Inc.Verified email at prateekjain.name

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
Finite-Sample Analysis of Proximal Gradient TD Algorithms. B Liu, J Liu, M Ghavamzadeh, S Mahadevan, M Petrik UAI, 504-513, 2015	115	2015
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
Safe policy improvement by minimizing robust baseline regret M Ghavamzadeh, M Petrik, Y Chow Advances in Neural Information Processing Systems 29, 2298-2306, 2016	86	2016
An Analysis of Laplacian Methods for Value Function Approximation in MDPs. M Petrik IJCAI, 2574-2579, 2007	76	2007
Learning parallel portfolios of algorithms M Petrik, S Zilberstein Annals of Mathematics and Artificial Intelligence 48 (1), 85-106, 2006	55	2006
A bilinear programming approach for multiagent planning M Petrik, S Zilberstein Journal of Artificial Intelligence Research 35, 235-274, 2009	42	2009
Average-Reward Decentralized Markov Decision Processes. M Petrik, S Zilberstein IJCAI, 1997-2002, 2007	39	2007
Biasing approximate dynamic programming with a lower discount factor M Petrik, B Scherrer Twenty-Second Annual Conference on Neural Information Processing Systems …, 2008	33	2008
Anytime coordination using separable bilinear programs M Petrik, S Zilberstein PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE 22 (1), 750, 2007	33	2007
Tight approximations of dynamic risk measures DA Iancu, M Petrik, D Subramanian Mathematics of Operations Research 40 (3), 655-682, 2015	32	2015
Constraint relaxation in approximate linear programs M Petrik, S Zilberstein Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	30	2009
Robust Approximate Bilinear Programming for Value Function Approximation. M Petrik, S Zilberstein Journal of Machine Learning Research 12 (10), 2011	29	2011
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	24	2017
An approximate solution method for large risk-averse Markov decision processes M Petrik, D Subramanian arXiv preprint arXiv:1210.4901, 2012	23	2012
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning. M Petrik, D Subramanian NIPS, 1979-1987, 2014	22	2014
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	20	2013
Hybrid least-squares algorithms for approximate policy evaluation J Johns, M Petrik, S Mahadevan Machine learning 76 (2), 243-256, 2009	19	2009
Beyond confidence regions: Tight bayesian ambiguity sets for robust mdps M Petrik, RH Russell arXiv preprint arXiv:1902.07605, 2019	18	2019
Policy-conditioned uncertainty sets for robust Markov decision processes A Tirinzoni, X Chen Advances in neural information processing systems, 2018	14	2018
Learning Heuristic Functions through Approximate Linear Programming. M Petrik, S Zilberstein ICAPS, 248-255, 2008	14	2008

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