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Csaba Szepesvári

Csaba Szepesvári

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Computer Science
Canada
2025

D-Index & Metrics

Computer Science

D-Index
74
Citations
25419
World Ranking
1474
National Ranking
49

Csaba Szepesvári publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Csaba Szepesvári sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 365 publications — 84th percentile

84% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Csaba Szepesvári D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Csaba Szepesvári sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 74 D-Index — 90th percentile

90% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Computer Science in Canada Leader Award
  • 2022 - Research.com Computer Science in Canada Leader Award

Overview

Csaba Szepesvári is affiliated with the University of Alberta in Canada. Their research focuses on computer science and decision sciences, with significant contributions in artificial intelligence and operations research.

The main topics of their work include:

  • Advanced Bandit Algorithms Research
  • Machine Learning and Algorithms
  • Reinforcement Learning in Robotics
  • Markov Chains and Monte Carlo Methods
  • Auction Theory and Applications
  • Optimization and Search Problems
  • Stochastic Gradient Optimization Techniques

Their publication record includes a wide range of venues, with the majority appearing in:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Machine Learning
  • Cambridge University Press eBooks

Several recent papers demonstrate the breadth of their research:

  • "Model-Based Reinforcement Learning with Value-Targeted Regression," published in 2020 at arXiv (Cornell University)
  • "Variational Policy Gradient Method for Reinforcement Learning with General Utilities," 2020, arXiv (Cornell University)
  • "Tighter risk certificates for neural networks," 2020, arXiv (Cornell University)
  • "On the Global Convergence Rates of Softmax Policy Gradient Methods," 2020, arXiv (Cornell University)
  • "Model Selection in Contextual Stochastic Bandit Problems," 2020, arXiv (Cornell University)

A significant publication includes a book titled "Bandit Algorithms," published in 2020 by Cambridge University Press.

Csaba Szepesvári frequently collaborates with other researchers including:

  • Tor Lattimore
  • Dale Schuurmans
  • András György
  • Gellért Weisz
  • Ilja Kuzborskij

Their work spans a range of scientific subfields such as:

  • Artificial Intelligence
  • Management Science and Operations Research
  • Statistics and Probability
  • Computational Theory and Mathematics
  • Computer Networks and Communications

Overall, the scope of Csaba Szepesvári's research integrates machine learning, optimization methods, and decision-making algorithms, contributing to both foundational theory and applied aspects in these domains.

Best Publications

  • Bandit based monte-carlo planning

    Levente Kocsis;Csaba Szepesvári

  • Bandit Algorithms

    Unknown

  • Algorithms for Reinforcement Learning

    Csaba Szepesvari

  • Improved Algorithms for Linear Stochastic Bandits

    Yasin Abbasi-yadkori;Dávid Pál;Csaba Szepesvári

  • Convergence Results for Single-Step On-PolicyReinforcement-Learning Algorithms

    Satinder Singh;Tommi Jaakkola;Michael L. Littman;Csaba Szepesvári

  • Exploration-exploitation tradeoff using variance estimates in multi-armed bandits

    Jean-Yves Audibert;Rémi Munos;Csaba Szepesvári

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

    Richard S. Sutton;Hamid Reza Maei;Doina Precup;Shalabh Bhatnagar

  • X -Armed Bandits

    Sébastien Bubeck;Rémi Munos;Gilles Stoltz;Csaba Szepesvári

  • Finite-Time Bounds for Fitted Value Iteration

    Rémi Munos;Csaba Szepesvári

  • Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path

    András Antos;Csaba Szepesvári;Rémi Munos

  • Parametric Bandits: The Generalized Linear Case

    Sarah Filippi;Olivier Cappe;Aurélien Garivier;Csaba Szepesvári

  • Learning with a Strong Adversary

    Ruitong Huang;Bing Xu;Dale Schuurmans;Csaba Szepesvari

  • The grand challenge of computer Go: Monte Carlo tree search and extensions

    Sylvain Gelly;Levente Kocsis;Marc Schoenauer;Michèle Sebag

  • Multi-criteria Reinforcement Learning

    Zoltán Gábor;Zsolt Kalmár;Csaba Szepesvári

  • Regret Bounds for the Adaptive Control of Linear Quadratic Systems

    Yasin Abbasi-Yadkori;Csaba Szepesvári

  • Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation

    Shalabh Bhatnagar;Doina Precup;David Silver;Richard S Sutton

  • Empirical Bernstein stopping

    Volodymyr Mnih;Csaba Szepesvári;Jean-Yves Audibert

  • Improved rates for the stochastic continuum-armed bandit problem

    Peter Auer;Ronald Ortner;Csaba Szepesvári

  • Apprenticeship learning using inverse reinforcement learning and gradient methods

    Gergely Neu;Csaba Szepesvári

  • Fitted Q-iteration in continuous action-space MDPs

    András Antos;Csaba Szepesvári;Rémi Munos

  • A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation

    Richard S Sutton;Hamid R. Maei;Csaba Szepesvári

  • Cascading Bandits: Learning to Rank in the Cascade Model

    Branislav Kveton;Csaba Szepesvari;Zheng Wen;Azin Ashkan

  • Online Learning under Delayed Feedback

    Pooria Joulani;Andras Gyorgy;Csaba Szepesvari

  • Model-Based Reinforcement Learning with Value-Targeted Regression.

    Zeyu Jia;Lin Yang;Csaba Szepesvári;Mengdi Wang

Frequent Co-Authors

András György
András György New York University Abu Dhabi
Branislav Kveton
Branislav Kveton Adobe Systems (United States)
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Mohammad Ghavamzadeh
Mohammad Ghavamzadeh Amazon (United States)
Eric Rogers
Eric Rogers University of Southampton
Dale Schuurmans
Dale Schuurmans University of Alberta
Venkatesh Saligrama
Venkatesh Saligrama Boston University
Craig Boutilier
Craig Boutilier Google (United States)
Jean-Yves Audibert
Jean-Yves Audibert Capital Fund Management (France)
Barnabás Póczos
Barnabás Póczos Carnegie Mellon University

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