Learning Static Parallel Portfolios of Algorithms. - Marek Petrik; Shlomo Zilberstein (AI&M, 2006)

Learning parallel portfolios of algorithms. - Marek Petrik; Shlomo Zilberstein (Ann. Math. Artif. Intell., 2006)

Anytime Coordination Using Separable Bilinear Programs. - Marek Petrik; Shlomo Zilberstein (AAAI, 2007)

An Analysis of Laplacian Methods for Value Function Approximation in MDPs. - Marek Petrik (IJCAI, 2007)

Average-Reward Decentralized Markov Decision Processes. - Marek Petrik; Shlomo Zilberstein (IJCAI, 2007)

Interaction Structure and Dimensionality Reduction in Decentralized MDPs. - Martin Allen; Marek Petrik; Shlomo Zilberstein (AAAI, 2008)

Learning Heuristic Functions through Approximate Linear Programming. - Marek Petrik; Shlomo Zilberstein (ICAPS, 2008)

A Successive Approximation Algorithm for Coordination Problems. - Marek Petrik; Shlomo Zilberstein (ISAIM, 2008)

Biasing Approximate Dynamic Programming with a Lower Discount Factor. - Marek Petrik; Bruno Scherrer (NIPS, 2008)

Constraint relaxation in approximate linear programs. - Marek Petrik; Shlomo Zilberstein (ICML, 2009)

Robust Value Function Approximation Using Bilinear Programming. - Marek Petrik; Shlomo Zilberstein (NIPS, 2009)

A Bilinear Programming Approach for Multiagent Planning. - Marek Petrik; Shlomo Zilberstein (J. Artif. Intell. Res., 2009)

Hybrid least-squares algorithms for approximate policy evaluation. - Jeffrey Johns; Marek Petrik; Sridhar Mahadevan (Mach. Learn., 2009)

Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. - Marek Petrik; Gavin Taylor; Ronald Parr; Shlomo Zilberstein (ICML, 2010)

Global Optimization for Value Function Approximation - Marek Petrik; Shlomo Zilberstein (CoRR, 2010)

Linear Dynamic Programs for Resource Management. - Marek Petrik; Shlomo Zilberstein (AAAI, 2011)

Robust Approximate Bilinear Programming for Value Function Approximation. - Marek Petrik; Shlomo Zilberstein (J. Mach. Learn. Res., 2011)

Learning Feature-Based Heuristic Functions. - Marek Petrik; Shlomo Zilberstein (Autonomous Search, 2012)

Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds. - Marek Petrik (ICML, 2012)

An Approximate Solution Method for Large Risk-Averse Markov Decision Processes. - Marek Petrik; Dharmashankar Subramanian (UAI, 2012)

Solution Methods for Constrained Markov Decision Process with Continuous Probability Modulation. - Marek Petrik; Dharmashankar Subramanian; Janusz Marecki (UAI, 2013)

Agile logistics simulation and optimization for managing disaster responses. - Francisco Barahona; Markus Ettl; Marek Petrik; Peter M. Rimshnick (WSC, 2013)

RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning. - Marek Petrik; Dharmashankar Subramanian (NIPS, 2014)

Social media and customer behavior analytics for personalized customer engagements. - Stephen J. Buckley; Markus Ettl; Prateek Jain 001; Ronny Luss; Marek Petrik; Rajesh Kumar Ravi; Chitra Venkatramani (IBM J. Res. Dev., 2014)

Efficient and accurate methods for updating generalized linear models with multiple feature additions. - Amit Dhurandhar; Marek Petrik (J. Mach. Learn. Res., 2014)

Finite-Sample Analysis of Proximal Gradient TD Algorithms. - Bo Liu 006; Ji Liu 002; Mohammad Ghavamzadeh; Sridhar Mahadevan; Marek Petrik (UAI, 2015)

Optimal Threshold Control for Energy Arbitrage with Degradable Battery Storage. - Marek Petrik; Xiaojian Wu (UAI, 2015)

Robust Partially-Compressed Least-Squares. - Stephen Becker; Ban Kawas; Marek Petrik; Karthikeyan Natesan Ramamurthy (CoRR, 2015)

Tight Approximations of Dynamic Risk Measures. - Dan Andrei Iancu; Marek Petrik; Dharmashankar Subramanian (Math. Oper. Res., 2015)

Proximal Gradient Temporal Difference Learning Algorithms. - Bo Liu 006; Ji Liu 002; Mohammad Ghavamzadeh; Sridhar Mahadevan; Marek Petrik (IJCAI, 2016)

Safe Policy Improvement by Minimizing Robust Baseline Regret. - Mohammad Ghavamzadeh; Marek Petrik; Yinlam Chow (NIPS, 2016)

Interpretable Policies for Dynamic Product Recommendations. - Marek Petrik; Ronny Luss (UAI, 2016)

Building an Interpretable Recommender via Loss-Preserving Transformation. - Amit Dhurandhar; Sechan Oh; Marek Petrik (CoRR, 2016)

Robust Partially-Compressed Least-Squares. - Stephen Becker; Ban Kawas; Marek Petrik (AAAI, 2017)

Value Directed Exploration in Multi-Armed Bandits with Structured Priors. - Bence Cserna; Marek Petrik; Reazul Hasan Russel; Wheeler Ruml (UAI, 2017)

A Practical Method for Solving Contextual Bandit Problems Using Decision Trees. - Adam N. Elmachtoub; Ryan McNellis; Sechan Oh; Marek Petrik (UAI, 2017)

Fast Bellman Updates for Robust MDPs. - Chin Pang Ho; Marek Petrik; Wolfram Wiesemann (ICML, 2018)

Low-rank Feature Selection for Reinforcement Learning. - Bahram Behzadian; Marek Petrik (ISAIM, 2018)

Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes. - Andrea Tirinzoni; Marek Petrik; Xiangli Chen; Brian D. Ziebart (NeurIPS, 2018)

Interpretable Reinforcement Learning with Ensemble Methods. - Alexander Brown; Marek Petrik (CoRR, 2018)

Tight Bayesian Ambiguity Sets for Robust MDPs. - Reazul Hasan Russel; Marek Petrik (CoRR, 2018)

Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity. - Bo Liu 006; Ian Gemp; Mohammad Ghavamzadeh; Ji Liu 002; Sridhar Mahadevan; Marek Petrik (J. Artif. Intell. Res., 2018)

Real-Time Planning as Decision-Making under Uncertainty. - Andrew Mitchell; Wheeler Ruml; Fabian Spaniol; Jörg Hoffmann 001; Marek Petrik (AAAI, 2019)

Fast Feature Selection for Linear Value Function Approximation. - Bahram Behzadian; Soheil Gharatappeh; Marek Petrik (ICAPS, 2019)

Inverse Reinforcement Learning of Interaction Dynamics from Demonstrations. - Mostafa Hussein; Momotaz Begum; Marek Petrik (ICRA, 2019)

Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs. - Marek Petrik; Reazul Hasan Russel (NeurIPS, 2019)

Robust Exploration with Tight Bayesian Plausibility Sets. - Reazul Hasan Russel; Tianyi Gu 001; Marek Petrik (CoRR, 2019)

High-Confidence Policy Optimization: Reshaping Ambiguity Sets in Robust MDPs. - Bahram Behzadian; Reazul Hasan Russel; Marek Petrik (CoRR, 2019)

Optimizing Norm-Bounded Weighted Ambiguity Sets for Robust MDPs. - Reazul Hasan Russel; Bahram Behzadian; Marek Petrik (CoRR, 2019)

Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search. - Maximilian Fickert; Tianyi Gu 001; Leonhard Staut; Wheeler Ruml; Jörg Hoffmann 001; Marek Petrik (AAAI, 2020)

Bayesian Robust Optimization for Imitation Learning. - Daniel S. Brown; Scott Niekum; Marek Petrik (NeurIPS, 2020)

Entropic Risk Constrained Soft-Robust Policy Optimization. - Reazul Hasan Russel; Bahram Behzadian; Marek Petrik (CoRR, 2020)

Finite-Sample Analysis of GTD Algorithms. - Bo Liu 006; Ji Liu 002; Mohammad Ghavamzadeh; Sridhar Mahadevan; Marek Petrik (CoRR, 2020)

Soft-Robust Algorithms for Handling Model Misspecification. - Elita A. Lobo; Mohammad Ghavamzadeh; Marek Petrik (CoRR, 2020)

Optimizing Percentile Criterion using Robust MDPs. - Bahram Behzadian; Reazul Hasan Russel; Marek Petrik; Chin Pang Ho (AISTATS, 2021)

Policy Gradient Bayesian Robust Optimization for Imitation Learning. - Zaynah Javed; Daniel S. Brown; Satvik Sharma; Jerry Zhu; Ashwin Balakrishna; Marek Petrik; Anca D. Dragan; Ken Goldberg (ICML, 2021)

Robust Behavior Cloning with Adversarial Demonstration Detection. - Mostafa Hussein; Brendan Crowe; Madison Clark-Turner; Paul Gesel; Marek Petrik; Momotaz Begum (IROS, 2021)

Fast Algorithms for $L_ infty$-constrained S-rectangular Robust MDPs. - Bahram Behzadian; Marek Petrik; Chin Pang Ho (NeurIPS, 2021)

Robust Maximum Entropy Behavior Cloning. - Mostafa Hussein; Brendan Crowe; Marek Petrik; Momotaz Begum (CoRR, 2021)

Partial Policy Iteration for L1-Robust Markov Decision Processes. - Chin Pang Ho; Marek Petrik; Wolfram Wiesemann (J. Mach. Learn. Res., 2021)

Robust $ phi$-Divergence MDPs. - Chin Pang Ho; Marek Petrik; Wolfram Wiesemann (NeurIPS, 2022)

Data poisoning attacks on off-policy policy evaluation methods. - Elita A. Lobo; Harvineet Singh; Marek Petrik; Cynthia Rudin; Himabindu Lakkaraju (UAI, 2022)

RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk. - Jia Lin Hau; Marek Petrik; Mohammad Ghavamzadeh; Reazul Hasan Russel (CoRR, 2022)

On the Convergence of Policy Gradient in Robust MDPs. - Qiuhao Wang; Chin Pang Ho; Marek Petrik (CoRR, 2022)

Entropic Risk Optimization in Discounted MDPs. - Jia Lin Hau; Marek Petrik; Mohammad Ghavamzadeh (AISTATS, 2023)

Policy Gradient in Robust MDPs with Global Convergence Guarantee. - Qiuhao Wang; Chin Pang Ho; Marek Petrik (ICML, 2023)

Percentile Criterion Optimization in Offline Reinforcement Learning. - Cyrus Cousins; Elita A. Lobo; Marek Petrik; Yair Zick (NeurIPS, 2023)

Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor. - Julien Grand-Clément; Marek Petrik (NeurIPS, 2023)

On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes. - Jia Lin Hau; Erick Delage; Mohammad Ghavamzadeh; Marek Petrik (NeurIPS, 2023)

Solving multi-model MDPs by coordinate ascent and dynamic programming. - Xihong Su; Marek Petrik (UAI, 2023)

On Dynamic Program Decompositions of Static Risk Measures. - Jia Lin Hau; Erick Delage; Mohammad Ghavamzadeh; Marek Petrik (CoRR, 2023)

A Convex Relaxation Approach to Bayesian Regret Minimization in Offline Bandits. - Mohammad Ghavamzadeh; Marek Petrik; Guy Tennenholtz (CoRR, 2023)

Beyond discounted returns: Robust Markov decision processes with average and Blackwell optimality. - Julien Grand-Clément; Marek Petrik; Nicolas Vieille (CoRR, 2023)

Bayesian Regret Minimization in Offline Bandits. - Marek Petrik; Guy Tennenholtz; Mohammad Ghavamzadeh (ICML, 2024)

ROIL: Robust Offline Imitation Learning without Trajectories. - Gersi Doko; Guang Yang; Daniel S. Brown; Marek Petrik (RLJ, 2024)

Non-adaptive Online Finetuning for Offline Reinforcement Learning. - Audrey Huang; Mohammad Ghavamzadeh; Nan Jiang 008; Marek Petrik (RLJ, 2024)

Stationary Policies are Optimal in Risk-averse Total-reward MDPs with EVaR. - Xihong Su; Marek Petrik; Julien Grand-Clément (CoRR, 2024)

Policy Gradient for Robust Markov Decision Processes. - Qiuhao Wang; Shaohang Xu; Chin Pang Ho; Marek Petrik (CoRR, 2024)

Risk-averse Total-reward MDPs with ERM and EVaR. - Xihong Su; Marek Petrik; Julien Grand-Clément (AAAI, 2025)

Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis. - Jia Lin Hau; Erick Delage; Esther Derman; Mohammad Ghavamzadeh; Marek Petrik (AISTATS, 2025)

On the Convex Formulations of Robust Markov Decision Processes. - Julien Grand-Clément; Marek Petrik (Math. Oper. Res., 2025)

Risk-Averse Total-Reward Reinforcement Learning. - Xihong Su; Jia Lin Hau; Gersi Doko; Kishan Panaganti; Marek Petrik (CoRR, 2025)

Convergence of Fast Policy Iteration in Markov Games and Robust MDPs. - Keith Badger; Marek Petrik; Jefferson Huang (CoRR, 2025)