World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
90
Citations
148193
World Ranking
589
National Ranking
316

Robert E. Schapire 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 Robert E. Schapire 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: 224 publications — 55th percentile

55% of scientists in this discipline score the same or lower.

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

Robert E. Schapire 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 Robert E. Schapire 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: 90 D-Index — 96th percentile

96% 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

  • 2016 - Member of the National Academy of Sciences
  • 2014 - Member of the National Academy of Engineering For contributions to machine learning through invention and development of boosting algorithms.
  • 2009 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to machine learning, including the theory and practice of boosting.
  • 2004 - ACM Paris Kanellakis Theory and Practice Award Theory and practice of boosting

Overview

Robert E. Schapire is affiliated with Microsoft in the United States. Their research spans several areas in computer science and decision sciences, with a focus on artificial intelligence and operations research. Schapire has produced scholarly work primarily published in venues such as arXiv (Cornell University), the Journal of the ACM, and Operations Research.

Their recent publications include:

  • Adversarial Bandits with Knapsacks, 2022, Journal of the ACM
  • Bayesian decision-making under misspecified priors with applications to meta-learning, 2021, arXiv (Cornell University)
  • Gradient descent follows the regularization path for general losses, 2020, arXiv (Cornell University)
  • Interactive Learning from Activity Description, 2021, arXiv (Cornell University)
  • Contextual Search in the Presence of Adversarial Corruptions, 2022, Operations Research

Frequent co-authors in their collaborative works include:

  • Miroslav Dudík
  • Akshay Krishnamurthy
  • Thodoris Lykouris
  • Dipendra Misra
  • Aldo Pacchiano

The main fields of study for Schapire's research are:

  • Computer Science
  • Decision Sciences

Within these fields, they have contributed to various subfields including:

  • Artificial Intelligence
  • Management Science and Operations Research
  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition

The primary topics covered in their work include:

  • Advanced Bandit Algorithms Research
  • Machine Learning and Algorithms
  • Reinforcement Learning in Robotics
  • Data Stream Mining Techniques
  • Auction Theory and Applications
  • Optimization and Search Problems
  • Adversarial Robustness in Machine Learning

The researcher has been recognized with multiple awards, such as:

  • Member of the National Academy of Sciences, 2016
  • Member of the National Academy of Engineering, 2014, for contributions to machine learning through invention and development of boosting algorithms
  • Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), 2009, for significant contributions to machine learning, including the theory and practice of boosting
  • ACM Paris Kanellakis Theory and Practice Award, 2004, for theory and practice of boosting

Best Publications

  • A Decision Theoretic Generalization of On-Line Learning and an Application to Boosting

    Y. Freund;R. Schapire

  • Experiments with a new boosting algorithm

    Yoav Freund;Robert E. Schapire

  • The Strength of Weak Learnability

    Robert E. Schapire

  • A Short Introduction to Boosting

    Yoav Freund;Robert E. Schapire

  • Improved boosting algorithms using confidence-rated predictions

    Robert E. Schapire;Yoram Singer

  • Boosting the margin: a new explanation for the effectiveness of voting methods

    Robert E. Schapire;Yoav Freund;Peter Bartlett;Wee Sun Lee

  • BoosTexter: A Boosting-based Systemfor Text Categorization

    Robert E. Schapire;Yoram Singer

  • A maximum entropy approach to species distribution modeling

    Steven J. Phillips;Miroslav Dudík;Robert E. Schapire

  • An efficient boosting algorithm for combining preferences

    Yoav Freund;Raj Iyer;Robert E. Schapire;Yoram Singer

  • The Boosting Approach to Machine Learning An Overview

    Robert E. Schapire

  • A contextual-bandit approach to personalized news article recommendation

    Lihong Li;Wei Chu;John Langford;Robert E. Schapire

  • Opening the black box: an open-source release of Maxent

    Steven J. Phillips;Robert P. Anderson;Robert P. Anderson;Miroslav Dudík;Robert E. Schapire

  • Reducing multiclass to binary: a unifying approach for margin classifiers

    Erin L. Allwein;Robert E. Schapire;Yoram Singer

  • The Nonstochastic Multiarmed Bandit Problem

    Peter Auer;Nicolò Cesa-Bianchi;Yoav Freund;Robert E. Schapire

  • A brief introduction to boosting

    Robert E. Schapire

  • Large margin classification using the perceptron algorithm

    Yoav Freund;Robert E. Schapire

  • Explaining AdaBoost

    Unknown

  • Boosting: Foundations and Algorithms

    Robert E. Schapire;Yoav Freund

  • Gambling in a rigged casino: The adversarial multi-armed bandit problem

    P. Auer;N. Cesa-Bianchi;Y. Freund;R. Schapire

  • Contextual bandits with linear Payoff functions

    Wei Chu;Lihong Li;Lev Reyzin;Robert E. Schapire

  • Strength of weak learnability

    Robert E. Schapire

  • Boosting the margin: A new explanation for the effectiveness of voting methods

    Robert E. Schapire;Yoav Freund;Peter Barlett;Wee Sun Lee

  • Foundations of Machine Learning

    Robert E. Schapire;Yoav Freund

Frequent Co-Authors

Yoav Freund
Yoav Freund University of California, San Diego
Miroslav Dudík
Miroslav Dudík Microsoft (United States)
Alekh Agarwal
Alekh Agarwal Google (United States)
Michael Kearns
Michael Kearns University of Pennsylvania
John Langford
John Langford Microsoft (United States)
Yoram Singer
Yoram Singer Princeton University
Cynthia Rudin
Cynthia Rudin Duke University
Akshay Krishnamurthy
Akshay Krishnamurthy Microsoft (United States)

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