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Csaba Szepesvári

Active 1993–2026

Also published as
Csaba Szepesvari
325
Papers
23,876
Citations
74
h-index
209
i10-index

Citations

Citations per year for Csaba Szepesvári1980: 1 citations1981: 1 citations1982: 1 citations1993: 1 citations1995: 1 citations1996: 3 citations1997: 11 citations1998: 15 citations1999: 11 citations2000: 19 citations2001: 29 citations2002: 34 citations2003: 35 citations2004: 28 citations2005: 31 citations2006: 29 citations2007: 67 citations2008: 128 citations2009: 148 citations2010: 281 citations2011: 399 citations2012: 449 citations2013: 445 citations2014: 457 citations2015: 502 citations2016: 538 citations2017: 517 citations2018: 661 citations2019: 1,080 citations2020: 1,413 citations2021: 1,666 citations2022: 998 citations2023: 850 citations2024: 707 citations2025: 561 citations2026: 179 citations1983–1992: no citations, so these years are not shown1994: no citations, so this year is not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 3,389 citing papers, 30.7% of this breakdownChina: 1,223 citing papers, 11.1% of this breakdownUnited Kingdom: 810 citing papers, 7.3% of this breakdownFrance: 659 citing papers, 6% of this breakdownCanada: 637 citing papers, 5.8% of this breakdownGermany: 445 citing papers, 4% of this breakdownJapan: 309 citing papers, 2.8% of this breakdownNetherlands: 295 citing papers, 2.7% of this breakdownAustralia: 270 citing papers, 2.4% of this breakdownIndia: 248 citing papers, 2.2% of this breakdownIsrael: 239 citing papers, 2.2% of this breakdownItaly: 232 citing papers, 2.1% of this breakdown
0%30.7%Other 20.7%

Institutions

Fields

  • Computer Science59.4%
  • Decision Sciences22%
  • Engineering10.4%
  • Mathematics1.5%
  • Physics and Astronomy1%
  • Social Sciences0.8%
  • Other4.9%

Topics

  • Reinforcement Learning in Robotics12.9%
  • Advanced Bandit Algorithms Research10.2%
  • Machine Learning and Algorithms4.3%
  • Artificial Intelligence in Games4.1%
  • Optimization and Search Problems2.2%
  • Adversarial Robustness in Machine Learning2.1%
  • Other64.2%

Coauthors

All papers

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  1. Bandit Algorithms

    Authors: Tor Lattimore, Csaba Szepesvári - Cambridge University Press eBooks 2020 cited by 851

  2. Bandit Based Monte-Carlo Planning

    Authors: Levente Kocsis, Csaba Szepesvári - Lecture notes in computer science, ECML 2006 cited by 2,867

  3. To Believe or Not to Believe Your LLM

    Authors: Yasin Abbasi Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári - arXiv (Cornell University), CoRR 2024 cited by 71

  4. Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms

    Authors: Satinder Singh, Tommi S. Jaakkola, Michael L. Littman, Csaba Szepesvári - Machine Learning, Mach. Learn. 1998 cited by 625

  5. Mitigating LLM Hallucinations via Conformal Abstention

    Authors: Yasin Abbasi Yadkori, Ilja Kuzborskij, David Stutz, András György, Adam Fisch, Arnaud Doucet, Iuliya Beloshapka, Wei-Hung Weng, Yao-Yuan Yang, Csaba Szepesvári, Ali Taylan Cemgil, Nenad Tomasev - arXiv (Cornell University), CoRR 2024 cited by 56

  6. Improved Algorithms for Linear Stochastic Bandits

    Authors: Yasin Abbasi-Yadkori, Dávid Pál, Csaba Szepesvári - http://papers.nips.cc/paper/4417-improved-algorithms-for-linear-stochastic-bandits.pdf 2011 cited by 2,073

  7. Exploration-exploitation tradeoff using variance estimates in multi-armed bandits

    Authors: Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári - Theoretical Computer Science, Theor. Comput. Sci. 2009 cited by 566

  8. Fast gradient-descent methods for temporal-difference learning with linear function approximation

    Authors: Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora - Conference on Machine Learning, ICML 2009 cited by 532

  9. Learning with a Strong Adversary

    Authors: Ruitong Huang, Bing Xu, Dale Schuurmans, Csaba Szepesvári - arXiv (Cornell University), CoRR 2015 cited by 265

  10. Behaviour Suite for Reinforcement Learning

    Authors: Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt - ICLR 2020 cited by 212

  11. Online Least Squares Estimation with Self-Normalized Processes: An Application to Bandit Problems

    Authors: Yasin Abbasi-Yadkori, Dávid Pál, Csaba Szepesvári - arXiv (Cornell University), CoRR 2011 cited by 61

  12. Empirical Bernstein stopping

    Authors: Volodymyr Mnih, Csaba Szepesvári, Jean-Yves Audibert - conference on Machine learning - ICML '08 2008 cited by 188

  13. A Unified Analysis of Value-Function-Based Reinforcement Learning Algorithms

    Authors: Csaba Szepesvári, Michael L. Littman - Neural Computation, Neural Comput. 1999 cited by 183

  14. Finite-Time Bounds for Fitted Value Iteration

    Authors: Rémi Munos, Csaba Szepesvári - http://www.sztaki.hu/~szcsaba/papers/munos08a.pdf, J. Mach. Learn. Res. 2008 cited by 264

  15. Sample-Efficient Reinforcement Learning of Partially Observable Markov Games

    Authors: Qinghua Liu, Csaba Szepesvári, Chi Jin - Advances in Neural Information Processing Systems 35, NeurIPS 2022 cited by 42

  16. Frontier LLMs Still Struggle with Simple Reasoning Tasks

    Authors: Alan Malek, Jiawei Ge, Nevena Lazic, Chi Jin, András György, Csaba Szepesvári - ArXiv.org, CoRR 2025 cited by 14

  17. When Is Partially Observable Reinforcement Learning Not Scary?

    Authors: Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin - COLT 2022 cited by 34

  18. Optimistic MLE - A Generic Model-based Algorithm for Partially Observable Sequential Decision Making

    Authors: Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin - Symposium on Theory of Computing, STOC 2023 cited by 24

  19. PAC-Bayes with Backprop

    Authors: Omar Rivasplata, Vikram M. Tankasali, Csaba Szepesvári - arXiv (Cornell University), CoRR 2019 cited by 40

  20. Tuning Bandit Algorithms in Stochastic Environments

    Authors: Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári - Lecture notes in computer science, ALT 2007 cited by 161

  21. Multi-criteria Reinforcement Learning

    Authors: Zoltán Gábor, Zsolt Kalmár, Csaba Szepesvári - http://victoria.mindmaker.hu/~szepes/papers/multi-rep97.ps.gz, ICML 1998 cited by 210

  22. Stochastic Low-Rank Bandits

    Authors: Branislav Kveton, Csaba Szepesvári, Anup Rao, Zheng Wen, Yasin Abbasi-Yadkori, S. Muthukrishnan - arXiv (Cornell University), CoRR 2017 cited by 33

  23. The Asymptotic Convergence-Rate of Q-learning

    Authors: Csaba Szepesvári - http://www.ualberta.ca/~szepesva/papers/nips97.ps.pdf 1997 cited by 183

  24. On Multi-objective Policy Optimization as a Tool for Reinforcement Learning

    Authors: Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra, Dhruva TB, Arunkumar Byravan, Konstantinos Bousmalis, András György, Csaba Szepesvári, Raia Hadsell, Nicolas Heess, Martin A. Riedmiller - arXiv (Cornell University), CoRR 2021 cited by 22