World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
86
Citations
46937
World Ranking
751
National Ranking
399

Peter L. Bartlett 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 Peter L. Bartlett 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: 348 publications — 81st percentile

81% of scientists in this discipline score the same or lower.

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

Peter L. Bartlett 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 Peter L. Bartlett 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: 86 D-Index — 95th percentile

95% 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

  • 2018 - ACM Fellow For contributions to the theory of machine learning

Overview

Peter L. Bartlett is affiliated with the University of California, Berkeley in the United States. Their work primarily focuses on computer science, with significant contributions to artificial intelligence, statistics and probability, management science and operations research, computational mechanics, and computer vision and pattern recognition.

The main subjects of their research encompass stochastic gradient optimization techniques, advanced bandit algorithms research, machine learning and algorithms, statistical methods and inference, sparse and compressive sensing techniques, reinforcement learning in robotics, and domain adaptation and few-shot learning.

Frequent coauthors include Michael I. Jordan, Aldo Pacchiano, Philip M. Long, Niladri S. Chatterji, and Martin J. Wainwright, with collaborations spanning multiple publications.

The scientist has published extensively, with key publication venues including arXiv (Cornell University), Bernoulli, Proceedings of the National Academy of Sciences, Acta Numerica, and the Journal of the American Statistical Association.

  • Self-Distillation Amplifies Regularization in Hilbert Space, 2020, arXiv (Cornell University)
  • Reinforcement Learning in POMDP's via Direct Gradient Ascent, 2025, arXiv (Cornell University)
  • Benign overfitting in ridge regression, 2020, arXiv (Cornell University)
  • Benign overfitting in linear regression, 2020, Proceedings of the National Academy of Sciences
  • Is there an analog of Nesterov acceleration for gradient-based MCMC?, 2021, Bernoulli

Peter L. Bartlett received the ACM Fellow award in 2018 for contributions to the theory of machine learning.

Best Publications

  • New Support Vector Algorithms

    Bernhard Schölkopf;Alex J. Smola;Robert C. Williamson;Peter L. Bartlett

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

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

  • Rademacher and gaussian complexities: risk bounds and structural results

    Peter L. Bartlett;Shahar Mendelson

  • Learning the Kernel Matrix with Semidefinite Programming

    Gert R. G. Lanckriet;Nello Cristianini;Peter Bartlett;Laurent El Ghaoui

  • Neural Network Learning: Theoretical Foundations

    Martin Anthony;Peter L. Bartlett

  • The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

    P.L. Bartlett

  • Convexity, Classification, and Risk Bounds

    Peter L Bartlett;Michael I Jordan;Jon D McAuliffe

  • Boosting Algorithms as Gradient Descent in Function Space

    Llew Mason;Jonathan Baxter;Peter Bartlett;Marcus Frean

  • Advances in Large Margin Classifiers

    Alexander J. Smola;Peter J. Bartlett

  • Infinite-horizon policy-gradient estimation

    Jonathan Baxter;Peter L. Bartlett

  • Boosting Algorithms as Gradient Descent

    Llew Mason;Jonathan Baxter;Peter L. Bartlett;Marcus R. Frean

  • Local Rademacher complexities

    Peter L. Bartlett;Olivier Bousquet;Shahar Mendelson

  • Probabilities for SV Machines

    Alexander J. Smola;Peter Bartlett;Bernhard Schölkopf;Dale Schuurmans

  • Structural risk minimization over data-dependent hierarchies

    J. Shawe-Taylor;P.L. Bartlett;R.C. Williamson;M. Anthony

  • Spectrally-normalized margin bounds for neural networks

    Peter L. Bartlett;Peter L. Bartlett;Dylan J. Foster;Matus Jan Telgarsky

  • Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

    Dong Yin;Yudong Chen;Kannan Ramchandran;Peter L. Bartlett

  • RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning

    Yan Duan;John Schulman;Xi Chen;Peter L. Bartlett

  • Benign overfitting in linear regression

    Peter L. Bartlett;Philip M. Long;Gábor Lugosi;Alexander Tsigler

  • Model Selection and Error Estimation

    Peter L. Bartlett;Stéphane Boucheron;Gábor Lugosi

  • Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning

    Evan Greensmith;Peter L. Bartlett;Jonathan Baxter

Frequent Co-Authors

Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Robert C. Williamson
Robert C. Williamson University of Tübingen
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Ambuj Tewari
Ambuj Tewari University of Michigan–Ann Arbor
Dale Schuurmans
Dale Schuurmans University of Alberta
Alekh Agarwal
Alekh Agarwal Google (United States)
Benjamin I. P. Rubinstein
Benjamin I. P. Rubinstein University of Melbourne

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