Marek Petrik

999 total citations
43 papers, 363 citations indexed

About

Marek Petrik is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computational Theory and Mathematics. According to data from OpenAlex, Marek Petrik has authored 43 papers receiving a total of 363 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Artificial Intelligence, 14 papers in Management Science and Operations Research and 11 papers in Computational Theory and Mathematics. Recurrent topics in Marek Petrik's work include Reinforcement Learning in Robotics (15 papers), Risk and Portfolio Optimization (6 papers) and Machine Learning and Algorithms (5 papers). Marek Petrik is often cited by papers focused on Reinforcement Learning in Robotics (15 papers), Risk and Portfolio Optimization (6 papers) and Machine Learning and Algorithms (5 papers). Marek Petrik collaborates with scholars based in United States, France and Hong Kong. Marek Petrik's co-authors include Shlomo Zilberstein, Dharmashankar Subramanian, Bruno Scherrer, Dan A. Iancu, Gavin Taylor, Ronald Parr, Markus Ettl, Ronny Luss, Francisco Barahona and Zhongming Lu and has published in prestigious journals such as Journal of Cleaner Production, Resources Conservation and Recycling and Machine Learning.

In The Last Decade

Marek Petrik

41 papers receiving 343 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Marek Petrik United States	12	213	94	54	54	51	43	363
Wang Guoxun China	5	107 0.5×	231 2.5×	34 0.6×	42 0.8×	73 1.4×	8	446
Stjepan Oreški Croatia	6	337 1.6×	44 0.5×	24 0.4×	40 0.7×	24 0.5×	9	531
Jisong Kou China	9	136 0.6×	37 0.4×	31 0.6×	59 1.1×	28 0.5×	26	296
Goran Oreški Croatia	6	315 1.5×	45 0.5×	21 0.4×	38 0.7×	25 0.5×	10	513
Jos Vrancken Netherlands	11	155 0.7×	39 0.4×	48 0.9×	80 1.5×	201 3.9×	62	468
Diego García‐Gil Spain	10	225 1.1×	27 0.3×	65 1.2×	23 0.4×	33 0.6×	18	427
Yang-Geng Fu China	13	273 1.3×	114 1.2×	34 0.6×	119 2.2×	40 0.8×	37	451
Hangjun Zhou China	10	99 0.5×	99 1.1×	46 0.9×	11 0.2×	42 0.8×	34	307
Yefan Han China	9	165 0.8×	261 2.8×	32 0.6×	47 0.9×	41 0.8×	13	429
Francisco Herrera Triguero Spain	10	200 0.9×	37 0.4×	59 1.1×	45 0.8×	38 0.7×	26	380

Countries citing papers authored by Marek Petrik

Since Specialization

Citations

This map shows the geographic impact of Marek Petrik's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marek Petrik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marek Petrik more than expected).

Fields of papers citing papers by Marek Petrik

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marek Petrik. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marek Petrik. The network helps show where Marek Petrik may publish in the future.

Co-authorship network of co-authors of Marek Petrik

This figure shows the co-authorship network connecting the top 25 collaborators of Marek Petrik. A scholar is included among the top collaborators of Marek Petrik based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marek Petrik. Marek Petrik is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Petrik, Marek, et al.. (2025). Deep reinforcement learning-based optimization of an island energy-water microgrid system. Resources Conservation and Recycling. 222. 108440–108440. 1 indexed citations

Petrik, Marek, et al.. (2024). On the Convex Formulations of Robust Markov Decision Processes. Mathematics of Operations Research. 50(3). 1681–1706.

Petrik, Marek, et al.. (2019). Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs. Neural Information Processing Systems. 32. 7049–7058. 2 indexed citations

Petrik, Marek, et al.. (2018). Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes. Neural Information Processing Systems. 31. 8939–8949. 2 indexed citations

Ho, Chin Pang, Marek Petrik, & Wolfram Wiesemann. (2018). Fast Bellman Updates for Robust MDPs. International Conference on Machine Learning. 1979–1988. 8 indexed citations

Petrik, Marek & Ronny Luss. (2016). Interpretable policies for dynamic product recommendations. Uncertainty in Artificial Intelligence. 607–616. 5 indexed citations

Petrik, Marek & Xiaojian Wu. (2015). Optimal threshold control for energy arbitrage with degradable battery storage. Uncertainty in Artificial Intelligence. 692–701. 3 indexed citations

Dhurandhar, Amit & Marek Petrik. (2014). Efficient and accurate methods for updating generalized linear models with multiple feature additions. Journal of Machine Learning Research. 15(1). 2607–2627. 4 indexed citations

Petrik, Marek & Dharmashankar Subramanian. (2014). RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning. neural information processing systems. 27. 1979–1987. 5 indexed citations

10.

Barahona, Francisco, et al.. (2013). Agile logistics simulation and optimization for managing disaster responses. Winter Simulation Conference. 3340–3351. 7 indexed citations

11.

Marecki, Janusz, Marek Petrik, & Dharmashankar Subramanian. (2013). Solution methods for constrained Markov decision process with continuous probability modulation. Uncertainty in Artificial Intelligence. 518–526. 4 indexed citations

12.

Huang, Pu, Dan A. Iancu, Marek Petrik, & Dharmashankar Subramanian. (2011). The Price of Dynamic Inconsistency for Distortion Risk Measures. arXiv (Cornell University). 5 indexed citations

13.

Petrik, Marek & Shlomo Zilberstein. (2011). Robust Approximate Bilinear Programming for Value Function Approximation. Journal of Machine Learning Research. 12(92). 3027–3063. 13 indexed citations

14.

Petrik, Marek & Shlomo Zilberstein. (2009). Robust Value Function Approximation Using Bilinear Programming. Neural Information Processing Systems. 22. 1446–1454. 1 indexed citations

15.

Petrik, Marek, et al.. (2008). Interaction structure and dimensionality reduction in decentralized MDPs. National Conference on Artificial Intelligence. 1440–1441. 5 indexed citations

16.

Petrik, Marek & Shlomo Zilberstein. (2008). Learning heuristic functions through approximate linear programming. International Conference on Automated Planning and Scheduling. 248–255. 5 indexed citations

17.

Petrik, Marek. (2007). An analysis of Laplacian methods for value function approximation in MDPs. International Joint Conference on Artificial Intelligence. 2574–2579. 38 indexed citations

18.

Petrik, Marek & Shlomo Zilberstein. (2007). Average-reward decentralized Markov decision processes. International Joint Conference on Artificial Intelligence. 1997–2002. 18 indexed citations

19.

Petrik, Marek & Shlomo Zilberstein. (2007). Anytime coordination using separable bilinear programs. National Conference on Artificial Intelligence. 750–755. 18 indexed citations

20.

Petrik, Marek & Shlomo Zilberstein. (2006). Learning Static Parallel Portfolios of Algorithms.. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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