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
Reazul Hasan RusselResearch Scientist at MetaVerified email at wildcats.unh.edu
Sechan OhMoloco, Previously at IBM, StanfordVerified email at molocoads.com
Ji LiuUniversity of RochesterVerified email at cs.rochester.edu
Bo LiuAssociate Professor, Auburn UniversityVerified email at cs.umass.edu
Adam N. ElmachtoubColumbia University, Dept. of Industrial Engineering and Operations ResearchVerified email at ieor.columbia.edu
Amit DhurandharPrincipal RSM, IBMVerified email at us.ibm.com
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 JainLivePersonVerified email at prateekjain.name
Janusz MareckiDeepMindVerified email at google.com
Francisco BarahonaIBM ResearchVerified email at us.ibm.com
Jeff JohnsMandiantVerified email at mandiant.com
Marco LaumannsVerified email at ethz.ch
Xiaojian WuFacebook AIVerified 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
Finite-sample analysis of proximal gradient td algorithms B Liu, J Liu, M Ghavamzadeh, S Mahadevan, M Petrik arXiv preprint arXiv:2006.14364, 2020	139	2020
Safe policy improvement by minimizing robust baseline regret M Ghavamzadeh, M Petrik, Y Chow Advances in Neural Information Processing Systems 29, 2016	108	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	87	2010
An Analysis of Laplacian Methods for Value Function Approximation in MDPs. M Petrik IJCAI, 2574-2579, 2007	79	2007
Learning parallel portfolios of algorithms M Petrik, S Zilberstein Annals of Mathematics and Artificial Intelligence 48 (1), 85-106, 2006	62	2006
Biasing approximate dynamic programming with a lower discount factor M Petrik, B Scherrer Advances in neural information processing systems 21, 2008	48	2008
A bilinear programming approach for multiagent planning M Petrik, S Zilberstein Journal of Artificial Intelligence Research 35, 235-274, 2009	44	2009
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	43	2017
Average-Reward Decentralized Markov Decision Processes. M Petrik, S Zilberstein IJCAI, 1997-2002, 2007	38	2007
Tight approximations of dynamic risk measures DA Iancu, M Petrik, D Subramanian Mathematics of Operations Research 40 (3), 655-682, 2015	36	2015
Constraint relaxation in approximate linear programs M Petrik, S Zilberstein Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	34	2009
Anytime coordination using separable bilinear programs M Petrik, S Zilberstein PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE 22 (1), 750, 2007	32	2007
RAAM: The benefits of robustness in approximating aggregated MDPs in reinforcement learning M Petrik, D Subramanian Advances in Neural Information Processing Systems 27, 2014	31	2014
Beyond confidence regions: Tight bayesian ambiguity sets for robust mdps M Petrik, RH Russel Advances in neural information processing systems 32, 2019	29	2019
Fast Bellman updates for robust MDPs CP Ho, M Petrik, W Wiesemann International Conference on Machine Learning, 1979-1988, 2018	29	2018
An approximate solution method for large risk-averse Markov decision processes M Petrik, D Subramanian arXiv preprint arXiv:1210.4901, 2012	28	2012
Robust Approximate Bilinear Programming for Value Function Approximation. M Petrik, S Zilberstein Journal of Machine Learning Research 12 (10), 2011	28	2011
Proximal Gradient Temporal Difference Learning Algorithms. B Liu, J Liu, M Ghavamzadeh, S Mahadevan, M Petrik IJCAI, 4195-4199, 2016	20	2016
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	20	2009

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