Csaba Szepesvári
Impact in
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- Advanced Bandit Algorithms Research
- Artificial Intelligence top 0.1%
- Reinforcement Learning in Robotics
- Artificial Intelligence in Games
- Machine Learning and Algorithms
- Evolutionary Algorithms and Applications
Papers in
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- Advanced Bandit Algorithms Research 75
- Auction Theory and Applications 11
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- Reinforcement Learning in Robotics 63
- Machine Learning and Algorithms 48
- Neural Networks and Applications 13
- Co-authors
- Levente Kocsis (7 shared papers)Rémi Munos (10 shared papers)Tor Lattimore (19 shared papers)Jean-Yves Audibert (5 shared papers)Michael L. Littman (2 shared papers)Yasin Abbasi-Yadkori (8 shared papers)Dávid Pál (8 shared papers)Satinder Pal Singh (1 shared paper)
- Journals
- Machine Learning (7 papers)Journal of Machine Learning Research (5 papers)Theoretical Computer Science (4 papers)Synthesis lectures on artificial intelligence and machine learning (3 papers)IEEE Transactions on Automatic Control (3 papers)
- Partner nations
- CanadaHungaryUnited States
In The Last Decade
Csaba Szepesvári
170 papers receiving 7.5k citations
Csaba Szepesvári's Hit Papers
Peers
Comparison fields: 5 of 152
- Management Science and Operations Research 2.7k
- Artificial Intelligence 5.4k
- Computer Networks and Communications 1.5k
- Computational Theory and Mathematics 1.0k
- Computer Vision and Pattern Recognition 845
Countries citing papers authored by Csaba Szepesvári
This map shows the geographic impact of Csaba Szepesvári'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 Csaba Szepesvári with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Csaba Szepesvári more than expected).
Fields of papers citing papers by Csaba Szepesvári
This network shows the impact of papers produced by Csaba Szepesvári. 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 Csaba Szepesvári. The network helps show where Csaba Szepesvári may publish in the future.
Co-authors
The 25 scholars most cited alongside Csaba Szepesvári, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 178 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Bandit Based Monte-Carlo Planning Hit paper breakdown → | 2006 | 1971 |
| 2 | Bandit Algorithms Hit paper breakdown → | 2020 | 552 |
| 3 | Algorithms for Reinforcement Learning Hit paper breakdown → | 2010 | 468 |
| 4 | 2000 | 440 | |
| 5 | Improved Algorithms for Linear Stochastic Bandits Hit paper breakdown → | 2011 | 339 |
| 6 | 2009 | 309 | |
| 7 | 2009 | 286 | |
| 8 | Multi-criteria Reinforcement Learning | 1998 | 156 |
| 9 | 1999 | 134 | |
| 10 | 2007 | 129 | |
| 11 | Finite-Time Bounds for Fitted Value Iteration | 2008 | 128 |
| 12 | 2008 | 115 | |
| 13 | X -Armed Bandits | 2011 | 113 |
| 14 | 2012 | 105 | |
| 15 | 2007 | 99 | |
| 16 | Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation | 2009 | 97 |
| 17 | Parametric Bandits: The Generalized Linear Case | 2010 | 96 |
| 18 | 2007 | 92 | |
| 19 | 2007 | 78 | |
| 20 | Online Optimization in X-Armed Bandits | 2008 | 77 |
About Csaba Szepesvári
Csaba Szepesvári is a scholar working on Management Science and Operations Research, Artificial Intelligence, Computer Networks and Communications, Statistics and Probability and Computational Theory and Mathematics, having authored 178 papers that have together received 8.0k indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (75 papers), Reinforcement Learning in Robotics (63 papers), Machine Learning and Algorithms (48 papers), Optimization and Search Problems (30 papers), Neural Networks and Applications (13 papers), Auction Theory and Applications (11 papers), Sparse and Compressive Sensing Techniques (9 papers) and Markov Chains and Monte Carlo Methods (9 papers). The work is most often cited by research in Management Science and Operations Research (2.7k citations), Artificial Intelligence (5.4k citations), Computer Networks and Communications (1.5k citations), Computational Theory and Mathematics (1.0k citations) and Computer Vision and Pattern Recognition (845 citations). Csaba Szepesvári has collaborated with scholars based in Canada, Hungary and United States. Frequent co-authors include Levente Kocsis, Rémi Munos, Tor Lattimore, Jean-Yves Audibert, Michael L. Littman, Yasin Abbasi-Yadkori, Dávid Pál, Satinder Pal Singh, Richard S. Sutton and Tommi Jaakkola. Their work appears in journals such as Machine Learning, Journal of Machine Learning Research, Theoretical Computer Science, Synthesis lectures on artificial intelligence and machine learning and IEEE Transactions on Automatic Control.
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.