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ORCIDMarek Petrik
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2020 – today
- 2024
[c48]Marek Petrik, Guy Tennenholtz, Mohammad Ghavamzadeh:
Bayesian Regret Minimization in Offline Bandits. ICML 2024- [i36]Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju:
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods. CoRR abs/2404.04714 (2024) - [i35]Elita A. Lobo, Cyrus Cousins, Yair Zick, Marek Petrik:
Percentile Criterion Optimization in Offline Reinforcement Learning. CoRR abs/2404.05055 (2024) - [i34]Xihong Su, Marek Petrik:
Solving Multi-Model MDPs by Coordinate Ascent and Dynamic Programming. CoRR abs/2407.06329 (2024) - [i33]Xihong Su, Marek Petrik, Julien Grand-Clément:
Stationary Policies are Optimal in Risk-averse Total-reward MDPs with EVaR. CoRR abs/2408.17286 (2024) - 2023
- [c47]Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh:
Entropic Risk Optimization in Discounted MDPs. AISTATS 2023: 47-76 - [c46]Qiuhao Wang, Chin Pang Ho, Marek Petrik:
Policy Gradient in Robust MDPs with Global Convergence Guarantee. ICML 2023: 35763-35797 - [c45]Cyrus Cousins, Elita A. Lobo, Marek Petrik, Yair Zick:
Percentile Criterion Optimization in Offline Reinforcement Learning. NeurIPS 2023 - [c44]Julien Grand-Clément, Marek Petrik:
Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor. NeurIPS 2023 - [c43]Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh, Marek Petrik:
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes. NeurIPS 2023 - [c42]Xihong Su, Marek Petrik:
Solving multi-model MDPs by coordinate ascent and dynamic programming. UAI 2023: 2016-2025 - [i32]Julien Grand-Clément, Marek Petrik:
Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor. CoRR abs/2302.00036 (2023) - [i31]Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh, Marek Petrik:
On Dynamic Program Decompositions of Static Risk Measures. CoRR abs/2304.12477 (2023) - [i30]Mohammad Ghavamzadeh, Marek Petrik, Guy Tennenholtz:
A Convex Relaxation Approach to Bayesian Regret Minimization in Offline Bandits. CoRR abs/2306.01237 (2023) - [i29]Julien Grand-Clément, Marek Petrik, Nicolas Vieille:
Beyond discounted returns: Robust Markov decision processes with average and Blackwell optimality. CoRR abs/2312.03618 (2023) - 2022
- [c41]Chin Pang Ho, Marek Petrik, Wolfram Wiesemann:
Robust $\phi$-Divergence MDPs. NeurIPS 2022 - [c40]Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju:
Data poisoning attacks on off-policy policy evaluation methods. UAI 2022: 1264-1274 - [i28]Chin Pang Ho, Marek Petrik, Wolfram Wiesemann:
Robust Phi-Divergence MDPs. CoRR abs/2205.14202 (2022) - [i27]Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh, Reazul Hasan Russel:
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk. CoRR abs/2209.04067 (2022) - [i26]Julien Grand-Clément, Marek Petrik:
On the convex formulations of robust Markov decision processes. CoRR abs/2209.10187 (2022) - [i25]Qiuhao Wang, Chin Pang Ho, Marek Petrik:
On the Convergence of Policy Gradient in Robust MDPs. CoRR abs/2212.10439 (2022) - 2021
- [j9]Chin Pang Ho, Marek Petrik, Wolfram Wiesemann:
Partial Policy Iteration for L1-Robust Markov Decision Processes. J. Mach. Learn. Res. 22: 275:1-275:46 (2021) - [c39]Bahram Behzadian, Reazul Hasan Russel, Marek Petrik, Chin Pang Ho:
Optimizing Percentile Criterion using Robust MDPs. AISTATS 2021: 1009-1017 - [c38]Zaynah Javed, Daniel S. Brown, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca D. Dragan, Ken Goldberg:
Policy Gradient Bayesian Robust Optimization for Imitation Learning. ICML 2021: 4785-4796 - [c37]Mostafa Hussein, Brendan Crowe, Madison Clark-Turner, Paul Gesel, Marek Petrik, Momotaz Begum:
Robust Behavior Cloning with Adversarial Demonstration Detection. IROS 2021: 7858-7864 - [c36]Bahram Behzadian, Marek Petrik, Chin Pang Ho:
Fast Algorithms for $L_\infty$-constrained S-rectangular Robust MDPs. NeurIPS 2021: 25982-25992 - [i24]Mostafa Hussein, Brendan Crowe, Marek Petrik, Momotaz Begum:
Robust Maximum Entropy Behavior Cloning. CoRR abs/2101.01251 (2021) - [i23]Zaynah Javed, Daniel S. Brown, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca D. Dragan, Ken Goldberg:
Policy Gradient Bayesian Robust Optimization for Imitation Learning. CoRR abs/2106.06499 (2021) - 2020
- [c35]Maximilian Fickert
, Tianyi Gu, Leonhard Staut, Wheeler Ruml, Jörg Hoffmann, Marek Petrik:
Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search. AAAI 2020: 9827-9834 - [c34]Daniel S. Brown, Scott Niekum, Marek Petrik:
Bayesian Robust Optimization for Imitation Learning. NeurIPS 2020 - [i22]Bo Liu, Ian Gemp, Mohammad Ghavamzadeh, Ji Liu, Sridhar Mahadevan, Marek Petrik:
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity. CoRR abs/2006.03976 (2020) - [i21]Chin Pang Ho, Marek Petrik, Wolfram Wiesemann:
Partial Policy Iteration for L1-Robust Markov Decision Processes. CoRR abs/2006.09484 (2020) - [i20]Reazul Hasan Russel, Bahram Behzadian, Marek Petrik:
Entropic Risk Constrained Soft-Robust Policy Optimization. CoRR abs/2006.11679 (2020) - [i19]Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik:
Finite-Sample Analysis of GTD Algorithms. CoRR abs/2006.14364 (2020) - [i18]Daniel S. Brown, Scott Niekum, Marek Petrik:
Bayesian Robust Optimization for Imitation Learning. CoRR abs/2007.12315 (2020) - [i17]Elita A. Lobo, Mohammad Ghavamzadeh, Marek Petrik:
Soft-Robust Algorithms for Handling Model Misspecification. CoRR abs/2011.14495 (2020)
2010 – 2019
- 2019
- [c33]Andrew Mitchell, Wheeler Ruml, Fabian Spaniol, Jörg Hoffmann, Marek Petrik:
Real-Time Planning as Decision-Making under Uncertainty. AAAI 2019: 2338-2345 - [c32]Bahram Behzadian, Soheil Gharatappeh, Marek Petrik:
Fast Feature Selection for Linear Value Function Approximation. ICAPS 2019: 601-609 - [c31]Mostafa Hussein, Momotaz Begum, Marek Petrik:
Inverse Reinforcement Learning of Interaction Dynamics from Demonstrations. ICRA 2019: 2267-2274 - [c30]Marek Petrik, Reazul Hasan Russel:
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs. NeurIPS 2019: 7047-7056 - [i16]Marek Petrik, Reazul Hasan Russel:
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs. CoRR abs/1902.07605 (2019) - [i15]Reazul Hasan Russel, Tianyi Gu, Marek Petrik:
Robust Exploration with Tight Bayesian Plausibility Sets. CoRR abs/1904.08528 (2019) - [i14]Bahram Behzadian, Reazul Hasan Russel, Marek Petrik:
High-Confidence Policy Optimization: Reshaping Ambiguity Sets in Robust MDPs. CoRR abs/1910.10786 (2019) - [i13]Reazul Hasan Russel, Bahram Behzadian, Marek Petrik:
Optimizing Norm-Bounded Weighted Ambiguity Sets for Robust MDPs. CoRR abs/1912.02696 (2019) - 2018
- [j8]Bo Liu, Ian Gemp, Mohammad Ghavamzadeh, Ji Liu, Sridhar Mahadevan, Marek Petrik:
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity. J. Artif. Intell. Res. 63: 461-494 (2018) - [c29]Chin Pang Ho, Marek Petrik, Wolfram Wiesemann:
Fast Bellman Updates for Robust MDPs. ICML 2018: 1984-1993 - [c28]Bahram Behzadian, Marek Petrik:
Low-rank Feature Selection for Reinforcement Learning. ISAIM 2018 - [c27]Andrea Tirinzoni, Marek Petrik, Xiangli Chen, Brian D. Ziebart:
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes. NeurIPS 2018: 8953-8963 - [i12]Alexander Brown, Marek Petrik:
Interpretable Reinforcement Learning with Ensemble Methods. CoRR abs/1809.06995 (2018) - [i11]Reazul Hasan Russel, Marek Petrik:
Tight Bayesian Ambiguity Sets for Robust MDPs. CoRR abs/1811.06512 (2018) - 2017
- [c26]Stephen Becker, Ban Kawas, Marek Petrik:
Robust Partially-Compressed Least-Squares. AAAI 2017: 1742-1748 - [c25]Bence Cserna, Marek Petrik, Reazul Hasan Russel, Wheeler Ruml:
Value Directed Exploration in Multi-Armed Bandits with Structured Priors. UAI 2017 - [c24]Adam N. Elmachtoub, Ryan McNellis, Sechan Oh, Marek Petrik:
A Practical Method for Solving Contextual Bandit Problems Using Decision Trees. UAI 2017 - [i10]Bence Cserna, Marek Petrik, Reazul Hasan Russel, Wheeler Ruml:
Value Directed Exploration in Multi-Armed Bandits with Structured Priors. CoRR abs/1704.03926 (2017) - [i9]Adam N. Elmachtoub, Ryan McNellis, Sechan Oh, Marek Petrik:
A Practical Method for Solving Contextual Bandit Problems Using Decision Trees. CoRR abs/1706.04687 (2017) - 2016
- [c23]Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik:
Proximal Gradient Temporal Difference Learning Algorithms. IJCAI 2016: 4195-4199 - [c22]Mohammad Ghavamzadeh, Marek Petrik, Yinlam Chow:
Safe Policy Improvement by Minimizing Robust Baseline Regret. NIPS 2016: 2298-2306 - [c21]Marek Petrik, Ronny Luss:
Interpretable Policies for Dynamic Product Recommendations. UAI 2016 - [i8]Amit Dhurandhar, Sechan Oh, Marek Petrik:
Building an Interpretable Recommender via Loss-Preserving Transformation. CoRR abs/1606.05819 (2016) - 2015
- [j7]Dan Andrei Iancu, Marek Petrik, Dharmashankar Subramanian:
Tight Approximations of Dynamic Risk Measures. Math. Oper. Res. 40(3): 655-682 (2015) - [c20]Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik:
Finite-Sample Analysis of Proximal Gradient TD Algorithms. UAI 2015: 504-513 - [c19]Marek Petrik, Xiaojian Wu:
Optimal Threshold Control for Energy Arbitrage with Degradable Battery Storage. UAI 2015: 692-701 - [i7]Stephen Becker, Ban Kawas, Marek Petrik, Karthikeyan Natesan Ramamurthy:
Robust Partially-Compressed Least-Squares. CoRR abs/1510.04905 (2015) - 2014
- [j6]Stephen J. Buckley, Markus Ettl, Prateek Jain, Ronny Luss, Marek Petrik, Rajesh Kumar Ravi, Chitra Venkatramani:
Social media and customer behavior analytics for personalized customer engagements. IBM J. Res. Dev. 58(5/6) (2014) - [j5]Amit Dhurandhar, Marek Petrik:
Efficient and accurate methods for updating generalized linear models with multiple feature additions. J. Mach. Learn. Res. 15(1): 2607-2627 (2014) - [c18]Marek Petrik, Dharmashankar Subramanian:
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning. NIPS 2014: 1979-1987 - [i6]Marek Petrik, Shlomo Zilberstein:
A Bilinear Programming Approach for Multiagent Planning. CoRR abs/1401.3461 (2014) - 2013
- [c17]Marek Petrik, Dharmashankar Subramanian, Janusz Marecki:
Solution Methods for Constrained Markov Decision Process with Continuous Probability Modulation. UAI 2013 - [c16]Francisco Barahona, Markus Ettl, Marek Petrik, Peter M. Rimshnick:
Agile logistics simulation and optimization for managing disaster responses. WSC 2013: 3340-3351 - [i5]Marek Petrik, Dharmashankar Subramanian, Janusz Marecki:
Solution Methods for Constrained Markov Decision Process with Continuous Probability Modulation. CoRR abs/1309.6857 (2013) - 2012
- [c15]Marek Petrik:
Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds. ICML 2012 - [c14]Marek Petrik, Dharmashankar Subramanian:
An Approximate Solution Method for Large Risk-Averse Markov Decision Processes. UAI 2012: 805-814 - [p1]Marek Petrik, Shlomo Zilberstein:
Learning Feature-Based Heuristic Functions. Autonomous Search 2012: 269-305 - [i4]Marek Petrik:
Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds. CoRR abs/1205.1782 (2012) - [i3]Marek Petrik, Dharmashankar Subramanian:
An Approximate Solution Method for Large Risk-Averse Markov Decision Processes. CoRR abs/1210.4901 (2012) - 2011
- [j4]Marek Petrik, Shlomo Zilberstein:
Robust Approximate Bilinear Programming for Value Function Approximation. J. Mach. Learn. Res. 12: 3027-3063 (2011) - [c13]Marek Petrik, Shlomo Zilberstein:
Linear Dynamic Programs for Resource Management. AAAI 2011: 1377-1383 - 2010
- [c12]Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein:
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. ICML 2010: 871-878 - [i2]Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein:
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. CoRR abs/1005.1860 (2010) - [i1]Marek Petrik, Shlomo Zilberstein:
Global Optimization for Value Function Approximation. CoRR abs/1006.2743 (2010)
2000 – 2009
- 2009
- [j3]Marek Petrik, Shlomo Zilberstein:
A Bilinear Programming Approach for Multiagent Planning. J. Artif. Intell. Res. 35: 235-274 (2009) - [j2]Jeffrey Johns, Marek Petrik, Sridhar Mahadevan:
Hybrid least-squares algorithms for approximate policy evaluation. Mach. Learn. 76(2-3): 243-256 (2009) - [c11]Marek Petrik, Shlomo Zilberstein:
Constraint relaxation in approximate linear programs. ICML 2009: 809-816 - [c10]Marek Petrik, Shlomo Zilberstein:
Robust Value Function Approximation Using Bilinear Programming. NIPS 2009: 1446-1454 - [c9]Jeffrey Johns, Marek Petrik, Sridhar Mahadevan:
Hybrid Least-Squares Algorithms for Approximate Policy Evaluation. ECML/PKDD (1) 2009: 9 - 2008
- [c8]Martin Allen, Marek Petrik, Shlomo Zilberstein:
Interaction Structure and Dimensionality Reduction in Decentralized MDPs. AAAI 2008: 1440-1441 - [c7]Marek Petrik, Shlomo Zilberstein:
Learning Heuristic Functions through Approximate Linear Programming. ICAPS 2008: 248-255 - [c6]Marek Petrik, Shlomo Zilberstein:
A Successive Approximation Algorithm for Coordination Problems. ISAIM 2008 - [c5]Marek Petrik, Bruno Scherrer:
Biasing Approximate Dynamic Programming with a Lower Discount Factor. NIPS 2008: 1265-1272 - 2007
- [c4]Marek Petrik, Shlomo Zilberstein:
Anytime Coordination Using Separable Bilinear Programs. AAAI 2007: 750-755 - [c3]Marek Petrik, Shlomo Zilberstein:
Average-Reward Decentralized Markov Decision Processes. IJCAI 2007: 1997-2002 - [c2]Marek Petrik:
An Analysis of Laplacian Methods for Value Function Approximation in MDPs. IJCAI 2007: 2574-2579 - 2006
- [j1]Marek Petrik, Shlomo Zilberstein:
Learning parallel portfolios of algorithms. Ann. Math. Artif. Intell. 48(1-2): 85-106 (2006) - [c1]Marek Petrik, Shlomo Zilberstein:
Learning Static Parallel Portfolios of Algorithms. AI&M 2006
Coauthor Index
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last updated on 2024-09-30 01:02 CEST by the dblp team
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