Marek Petrik 个人学术档案 - 学术资源搜索

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引用	2124	1398
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Mohammad GhavamzadehAmazon在 amazon.com 的电子邮件经过验证
Shlomo ZilbersteinProfessor of Computer Science, University of Massachusetts Amherst在 cs.umass.edu 的电子邮件经过验证
Dharmashankar SubramanianPrincipal Research Staff Member/Manager, IBM Research在 us.ibm.com 的电子邮件经过验证
Reazul Hasan RusselResearch Scientist at Meta在 wildcats.unh.edu 的电子邮件经过验证
Chin Pang HoCity University of Hong Kong在 cityu.edu.hk 的电子邮件经过验证
Bahram BehzadianMeta在 meta.com 的电子邮件经过验证
Sridhar MahadevanDirector, Data Science Lab, Adobe Research & Professor, University of Massachusetts, Amherst在 cs.umass.edu 的电子邮件经过验证
Sechan OhMoloco, Previously at IBM, Stanford在 molocoads.com 的电子邮件经过验证
Ji LiuMeta在 meta.com 的电子邮件经过验证
Bo LiuPhD, AAAI SM, IEEE SM在 cs.umass.edu 的电子邮件经过验证
Wolfram WiesemannProfessor of Analytics and Operations, Imperial College Business School在 imperial.ac.uk 的电子邮件经过验证
Daniel S. BrownAssistant Professor, Robotics Center and Kahlert School of Computing, University of Utah在 cs.utah.edu 的电子邮件经过验证
Yinlam ChowResearch Scientist, Google Research在 google.com 的电子邮件经过验证
Amit DhurandharPrincipal Research Scientist, IBM在 us.ibm.com 的电子邮件经过验证
Elita LoboUniversity of Massachusetts Amherst在 cs.umass.edu 的电子邮件经过验证
Adam N. ElmachtoubColumbia University, Dept. of Industrial Engineering and Operations Research在 ieor.columbia.edu 的电子邮件经过验证
Wheeler RumlUniversity of New Hampshire在 cs.unh.edu 的电子邮件经过验证
Dan A. IancuAssociate Professor of Operations, Information and Technology, Stanford University在 stanford.edu 的电子邮件经过验证
Prateek JainThe Tradedesk在 prateekjain.name 的电子邮件经过验证
Gavin TaylorUS Naval Academy在 usna.edu 的电子邮件经过验证

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Marek Petrik

University of New Hampshire

在 cs.unh.edu 的电子邮件经过验证 - 首页

Machine Learning


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	175	2020
Safe policy improvement by minimizing robust baseline regret M Ghavamzadeh, M Petrik, Y Chow Advances in Neural Information Processing Systems 29, 2016	153	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	91	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	70	2008
Fast Bellman updates for robust MDPs CP Ho, M Petrik, W Wiesemann International Conference on Machine Learning, 1979-1988, 2018	68	2018
Beyond confidence regions: Tight bayesian ambiguity sets for robust mdps M Petrik, RH Russel Advances in neural information processing systems 32, 2019	67	2019
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	61	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	55	2021
Tight approximations of dynamic risk measures DA Iancu, M Petrik, D Subramanian Mathematics of Operations Research 40 (3), 655-682, 2015	46	2015
Constraint relaxation in approximate linear programs M Petrik, S Zilberstein Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	46	2009
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	38	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	33	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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