Matej Moravčík

877 total citations · 1 hit paper
7 papers, 424 citations indexed

About

Matej Moravčík is a scholar working on Artificial Intelligence, Economics and Econometrics and Clinical Psychology. According to data from OpenAlex, Matej Moravčík has authored 7 papers receiving a total of 424 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Economics and Econometrics and 2 papers in Clinical Psychology. Recurrent topics in Matej Moravčík's work include Artificial Intelligence in Games (7 papers), Sports Analytics and Performance (5 papers) and Gambling Behavior and Treatments (2 papers). Matej Moravčík is often cited by papers focused on Artificial Intelligence in Games (7 papers), Sports Analytics and Performance (5 papers) and Gambling Behavior and Treatments (2 papers). Matej Moravčík collaborates with scholars based in Czechia, Canada and United Kingdom. Matej Moravčík's co-authors include Martin Schmid, Neil Burch, Michael Bowling, Kevin Waugh, Nolan Bard, Dustin Morrill, Trevor Davis, Viliam Lisý, Michael Johanson and Milan Hladík and has published in prestigious journals such as Science, Science Advances and arXiv (Cornell University).

In The Last Decade

Matej Moravčík

7 papers receiving 407 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

DeepStack: Expert-level artificial intelligence in heads-up no-limit poker

2017 409 citations Matej Moravčík, Martin Schmid et al. Science profile →

Peers

Matej Moravčík

AI

EE

SPS

MSOR

CP

Dustin Morrill Canada

Trevor Davis United States

Nolan Bard Canada

Umang Gupta India

Christoph Dann United States

Avraham Ruderman Australia

Sayash Kapoor United States

Maryam Heidari United States

Xiangling Fu China

Ananya Ganesh United States

Dustin Morrill Canada

Matej Moravčík

285 ×1.0

AI

71 ×1.0

EE

58 ×0.9

SPS

69 ×1.2

MSOR

37 ×0.9

CP

Citations per year, relative to Matej Moravčík Matej Moravčík (= 1×) peers Dustin Morrill

Countries citing papers authored by Matej Moravčík

Since Specialization

Citations

This map shows the geographic impact of Matej Moravčík'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 Matej Moravčík with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matej Moravčík more than expected).

Fields of papers citing papers by Matej Moravčík

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matej Moravčík. 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 Matej Moravčík. The network helps show where Matej Moravčík may publish in the future.

Co-authorship network of co-authors of Matej Moravčík

This figure shows the co-authorship network connecting the top 25 collaborators of Matej Moravčík. A scholar is included among the top collaborators of Matej Moravčík 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 Matej Moravčík. Matej Moravčík is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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7 of 7 papers shown

1.

Schmid, Martin, Matej Moravčík, Neil Burch, et al.. (2023). Student of Games: A unified learning algorithm for both perfect and imperfect information games. Science Advances. 9(46). eadg3256–eadg3256. 1 indexed citations

2.

Burch, Neil, et al.. (2018). AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 2 indexed citations

3.

Moravčík, Matej, Martin Schmid, Neil Burch, et al.. (2017). DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science. 356(6337). 508–513. 409 indexed citations breakdown →

4.

Moravčík, Matej, et al.. (2016). Refining Subgames in Large Imperfect Information Games. Proceedings of the AAAI Conference on Artificial Intelligence. 30(1). 5 indexed citations

5.

Burch, Neil, Martin Schmid, Matej Moravčík, & Michael Bowling. (2016). AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games. arXiv (Cornell University). 949–956. 2 indexed citations

6.

Schmid, Martin, et al.. (2015). Automatic Public State Space Abstraction in Imperfect Information Games. National Conference on Artificial Intelligence. 1 indexed citations

7.

Schmid, Martin, Matej Moravčík, & Milan Hladík. (2014). Bounding the Support Size in Extensive Form Games with Imperfect Information. Proceedings of the AAAI Conference on Artificial Intelligence. 28(1). 4 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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