Publications

2017

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

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

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

2016

Safe Policy Improvement by Minimizing Robust Baseline Regret, Marek Petrik, Yinlam Chow, Mohammad Ghavamzadeh, Conference on Neural Information Processing Systems (NIPS), 2016.

Interpretable Policies for Dynamic Product Recommendations, Marek Petrik, Ronny Luss, Uncertainty in Artificial Intelligence (UAI), 2016.

Building an Interpretable Recommender via Loss-Preserving Transformation, Amit Dhurandhar, Sechan Oh, Marek Petrik, 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016).

Safe Policy Improvement by Minimizing Robust Baseline Regret Marek Petrik, Yinlam Chow, Mohammad Ghavamzadeh, 2016 ICML Workshop on Reliable Machine Learning in the Wild.

2015

Robust Policy Optimization with Baseline Guarantees, Yinlam Chow, Marek Petrik, Mohammad Ghavamzadeh, arXiv:1506.04514.

Robust Partially-Compressed Least-Squares, Stephen Becker, Ban Kawas, Marek Petrik, Karthikeyan N. Ramamurthy, arXiv:1510.04905.

Tight Approximations of Dynamic Risk Measures, Dan Iancu, Marek Petrik, Dharmashankar Subramanian, Mathematics of Operations Research, 40(3), 2015.

Finite-Sample Analysis of Proximal Gradient TD Algorithms, Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik, Uncertainty in Artificial Intelligence (UAI), 2015, (Best Student Paper Award). [Appendix]

Optimal Threshold Control for Energy Arbitrage with Degradable Battery Storage, Marek Petrik, Xiaojian Wu, Uncertainty in Artificial Intelligence (UAI), 2015. [Appendix]

2014

RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning, Marek Petrik, Dharmashankar Subramanian, Conference on Neural Information Processing Systems (NIPS), (spotlight), 2014. [Full Paper].

Efficient and Accurate Methods for Updating Generalized Linear Models with Multiple Feature Additions, Amit Dhurandhar, Marek Petrik, Journal of Machine Learning Research 15:2607-2627, 2014. [bib]

Combining Social Media and Customer Behavior Analytics for Personalized Customer Engagements, Markus Ettl, Prateek Jain, Ronny Luss, Marek Petrik, Rajesh Ravi, Chitra Venkatramani, IBM Journal of Research Development, 58 (56) 7:1-7:12, 2014.

2013

Optimizing Deliveries in Agile Supply Chains with Demand Shocks, Francisco Barahona, Markus Ettl, Marek Petrik, Peter Rimshnick, Winter Simulation Conference, 2013.

Solution Methods for Constrained Markov Decision Process with Continuous Probability Modulation, Janusz Marecki, Marek Petrik, Dharmashankar Subramanian, Conference on Uncertainty in Artificial Intelligence (UAI), 2013.

2012

An Approximate Solution Method for Large Risk-Averse Markov Decision Processes, Marek Petrik and Dharmashankar Subramanian. Conference on Uncertainty in Artificial Intelligence (UAI), 2012.

Distributionally Robust Approach to Approximate Dynamic Programming, Marek Petrik, International Conference on Machine Learning (ICML), 2012. Also presented at European Workshop on Reinforcement Learning, 2012. Extended Technical Report (includes proofs).

Optimizing the end-to-end value chain through demand shaping and advanced customer analytics, Brenda Dietrich, Markus Ettl, Roger D. Lederman, Marek Petrik, 11th International Symposium on Process Systems Engineering, 2012.

2011

The Price of Dynamic Inconsistency for Distortion Risk Measures, Pu Huang, Dan Iancu, Marek Petrik, Dharmashankar Subramanian. Technical Report, 2011.

Linear Dynamic Programs for Resource Management, Marek Petrik and Shlomo Zilberstein, Conference on Artificial Intelligence (AAAI) [Computational Sustainability Track], 2011.

Robust Approximate Bilinear Programming for Value Function Approximation, Marek Petrik and Shlomo Zilberstein, Journal of Machine Learning Research 12(Oct):3027-3063, 2011.

2010

Optimization-based Approximate Dynamic Programming, Marek Petrik, Ph.D. Dissertation, 2010. Also, the original double-spaced version, and the defense presentation.

Feature Selection Using Regularization in Approximate Linear Program for Markov Decision Processes, Marek Petrik, Gavin Taylor, Ron Parr, Shlomo Zilberstein. International Conference on Machine Learning (ICML) 27, 2010. Technical Report (includes proofs and algorithms): arXiv 1005.1860.

2009

Robust Value Function Approximation Using Bilinear Programming, Marek Petrik and Shlomo Zilberstein, Conference on Neural Information Processing Systems (NIPS) 22 (spotlight), 2009. Technical Report (includes proofs) UM-CS-2009-052.

A Bilinear Programming Approach for Multiagent Planning, Marek Petrik and Shlomo Zilberstein, Journal of Artificial Intelligence Research 35:235-274, 2009.

Hybrid Least-Squares Algorithms for Approximate Policy Evaluation, Jeff Johns, Marek Petrik, Sridhar Mahadevan, European Conference on Machine Learning, and Machine Learning journal, 2009.

Constraint Relaxation in Approximate Linear Programs, Marek Petrik and Shlomo Zilberstein, International Conference on Machine Learning (ICML), 2009.

Robust Approximate Optimization for Large Scale Planning Problems, Marek Petrik, AAAI Doctoral Consortium, 2009.

Blood Management Using Approximate Linear Programming, Marek Petrik and Shlomo Zilberstein, Presented at INFORMS Computing Society Meeting, Charleston, SC, 2009.

2008

A Successive Approximation Algorithm for Coordination Problems, Marek Petrik and Shlomo Zilberstein, 9th International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale, Florida, 2008.

Biasing Approximate Dynamic Programming with a Lower Discount Factor,Marek Petrik and Bruno Scherrer, Conference on Neural Information Processing Systems (NIPS), 2008.

Learning Heuristic Functions Through Approximate Linear Programming, Marek Petrik and Shlomo Zilberstein, International Conference on Automated Planning and Scheduling (ICAPS), 2008.

Interaction Structure and Dimensionality Reduction in Decentralized MDPs Martin Allen, Marek Petrik, Shlomo Zilberstein, The National Conference on Artificial Intelligence (AAAI), 2008. Extented technical report #UM-CS-2008-11.

2007

Anytime Coordination Using Separable Bilinear Programs, Marek Petrik, Shlomo Zilberstein, National Conference on Artificial Intelligence (AAAI), 2007.

An Analysis of Laplacian Methods for Value Function Approximation in MDPs, Marek Petrik, International Joint Conference on Artificial Intelligence (IJCAI), 2007.

Average-Reward Decentralized Markov Decision Processes, Marek Petrik, Shlomo Zilberstein, International Joint Conference on Artificial Intelligence (IJCAI), 2007.

2006

Learning Parallel Portfolios of Algorithms, Marek Petrik, Shlomo Zilberstein, Annals of Mathematics and Artificial Intelligence, 48(1-2):85-106, 2006

Learning Static Parallel Portfolios of Algorithms, Marek Petrik, Shlomo Zilberstein, International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale, 2006.

Learning Parallel Portfolios of Algorithms, Marek Petrik, Diploma Thesis at Univerzita Komenskeho, June 7th 2005. The code, and presentation are also available.

Statistically Optimal Combination of Algorithms, Marek Petrik, SOFSEM, 2005. (Best Student Poster).