Csaba Szepesvári
Impact in
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- Advanced Bandit Algorithms Research
- Artificial Intelligence top 0.2%
- Reinforcement Learning in Robotics
- Machine Learning and Algorithms
- Evolutionary Algorithms and Applications
- Data Stream Mining Techniques
Papers in
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- Reinforcement Learning in Robotics 62
- Machine Learning and Algorithms 38
- Neural Networks and Applications 10
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- Advanced Bandit Algorithms Research 70
- Auction Theory and Applications 11
- Co-authors
- Rémi Munos (10 shared papers)Tor Lattimore (13 shared papers)Yasin Abbasi-Yadkori (10 shared papers)Michael L. Littman (2 shared papers)Dávid Pál (7 shared papers)Jean-Yves Audibert (4 shared papers)Richard S. Sutton (7 shared papers)Tommi Jaakkola (1 shared paper)
- Journals
- Machine Learning (7 papers)Journal of Machine Learning Research (5 papers)Theoretical Computer Science (4 papers)IEEE Transactions on Automatic Control (3 papers)International Journal of Neural Systems (2 papers)
- Partner nations
- CanadaHungaryUnited States
In The Last Decade
Csaba Szepesvári
151 papers receiving 4.7k citations
Csaba Szepesvári's Hit Papers
Peers
Comparison fields: 5 of 142
- Management Science and Operations Research 2.0k
- Artificial Intelligence 3.1k
- Computer Networks and Communications 1.2k
- Computational Theory and Mathematics 727
- Control and Systems Engineering 627
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 161 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Bandit Algorithms Hit paper breakdown → | 2020 | 483 |
| 2 | 2000 | 408 | |
| 3 | Algorithms for Reinforcement Learning Hit paper breakdown → | 2010 | 407 |
| 4 | Improved Algorithms for Linear Stochastic Bandits Hit paper breakdown → | 2011 | 320 |
| 5 | 2009 | 260 | |
| 6 | 2009 | 260 | |
| 7 | Multi-criteria Reinforcement Learning | 1998 | 136 |
| 8 | 1999 | 122 | |
| 9 | Finite-Time Bounds for Fitted Value Iteration | 2008 | 111 |
| 10 | Parametric Bandits: The Generalized Linear Case | 2010 | 107 |
| 11 | 2007 | 99 | |
| 12 | X -Armed Bandits | 2011 | 95 |
| 13 | Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation | 2009 | 82 |
| 14 | 2012 | 82 | |
| 15 | A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation | 2008 | 71 |
| 16 | Fitted Q-iteration in continuous action-space MDPs | 2007 | 68 |
| 17 | Online Optimization in X-Armed Bandits | 2008 | 68 |
| 18 | The Asymptotic Convergence-Rate of Q-learning | 1997 | 64 |
| 19 | A convergent O ( n ) algorithm for off-policy temporal-difference learning with linear function approximation | 2008 | 63 |
| 20 | Improved Monte-Carlo Search | 2006 | 63 |
About Csaba Szepesvári
Csaba Szepesvári is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Computational Theory and Mathematics and Control and Systems Engineering, having authored 161 papers that have together received 5.0k indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (70 papers), Reinforcement Learning in Robotics (62 papers), Machine Learning and Algorithms (38 papers), Optimization and Search Problems (27 papers), Auction Theory and Applications (11 papers), Neural Networks and Applications (10 papers), Sparse and Compressive Sensing Techniques (8 papers) and Markov Chains and Monte Carlo Methods (7 papers). The work is most often cited by research in Management Science and Operations Research (2.0k citations), Artificial Intelligence (3.1k citations), Computer Networks and Communications (1.2k citations), Computational Theory and Mathematics (727 citations) and Control and Systems Engineering (627 citations). Csaba Szepesvári has collaborated with scholars based in Canada, Hungary and United States. Frequent co-authors include Rémi Munos, Tor Lattimore, Yasin Abbasi-Yadkori, Michael L. Littman, Dávid Pál, Jean-Yves Audibert, Richard S. Sutton, Tommi Jaakkola, Satinder Singh and Hamid Reza Maei. Their work appears in journals such as Machine Learning, Journal of Machine Learning Research, Theoretical Computer Science, IEEE Transactions on Automatic Control and International Journal of Neural Systems.
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.