World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
70
Citations
26319
World Ranking
1840
National Ranking
937

Nathan Srebro 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 Nathan Srebro 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: 188 publications — 42nd percentile

42% of scientists in this discipline score the same or lower.

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

Nathan Srebro 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 Nathan Srebro 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: 70 D-Index — 87th percentile

87% of scientists in this discipline score the same or lower.

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

Overview

Nathan Srebro is affiliated with the Toyota Technological Institute at Chicago in the United States. Their research primarily intersects the field of Computer Science, with a strong emphasis on subfields including Artificial Intelligence, Computational Mechanics, Statistics and Probability, Computer Vision and Pattern Recognition, and Electrical and Electronic Engineering.

The main topics in Nathan Srebro's research contributions cover a variety of areas related to machine learning and optimization. These include:

  • Machine Learning and Algorithms
  • Stochastic Gradient Optimization Techniques
  • Sparse and Compressive Sensing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Adversarial Robustness in Machine Learning
  • Machine Learning and Data Classification
  • Neural Networks and Applications

Nathan Srebro has coauthored numerous publications with frequent collaborators such as Gal Vardi, Blake Woodworth, Ohad Shamir, Daniel Soudry, and Lijia Zhou. These collaborators have appeared repeatedly in Srebro's work, indicating ongoing academic partnerships.

Their publication record includes a range of papers across several venues. Some significant recent papers are:

  • Lower bounds for non-convex stochastic optimization, 2022, Mathematical Programming
  • Is Local SGD Better than Minibatch SGD?, 2020, arXiv (Cornell University)
  • Minibatch vs Local SGD for Heterogeneous Distributed Learning, 2020, arXiv (Cornell University)
  • Does Invariant Risk Minimization Capture Invariance?, 2021, arXiv (Cornell University)
  • Fair Learning with Private Demographic Data, 2020, arXiv (Cornell University)

The majority of Nathan Srebro's work has been disseminated through arXiv (Cornell University), accounting for 61 publications. Other venues include Mathematical Programming, the Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, the ACM / IMS Journal of Data Science, and the Journal of Statistical Mechanics Theory and Experiment, reflecting a diverse range of publication outlets.

Best Publications

  • Pegasos: primal estimated sub-gradient solver for SVM

    Shai Shalev-Shwartz;Yoram Singer;Nathan Srebro;Andrew Cotter

  • Equality of opportunity in supervised learning

    Moritz Hardt;Eric Price;Nathan Srebro

  • Pegasos: Primal Estimated sub-GrAdient SOlver for SVM

    Shai Shalev-Shwartz;Yoram Singer;Nathan Srebro

  • Maximum-Margin Matrix Factorization

    Nathan Srebro;Jason Rennie;Tommi S. Jaakkola

  • Fast maximum margin matrix factorization for collaborative prediction

    Jasson D. M. Rennie;Nathan Srebro

  • Exploring Generalization in Deep Learning

    Behnam Neyshabur;Srinadh Bhojanapalli;David McAllester;Nathan Srebro

  • Weighted low-rank approximations

    Nathan Srebro;Tommi Jaakkola

  • The Marginal Value of Adaptive Gradient Methods in Machine Learning

    Ashia C. Wilson;Rebecca Roelofs;Mitchell Stern;Nathan Srebro

  • Communication-Efficient Distributed Optimization using an Approximate Newton-type Method

    Ohad Shamir;Nati Srebro;Tong Zhang

  • The implicit bias of gradient descent on separable data

    Daniel Soudry;Elad Hoffer;Mor Shpigel Nacson;Suriya Gunasekar

  • Rank, trace-norm and max-norm

    Nathan Srebro;Adi Shraibman

  • Norm-Based Capacity Control in Neural Networks

    Behnam Neyshabur;Ryota Tomioka;Nathan Srebro

  • In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

    Behnam Neyshabur;Ryota Tomioka;Nathan Srebro

  • Learnability, Stability and Uniform Convergence

    Shai Shalev-Shwartz;Ohad Shamir;Nathan Srebro;Karthik Sridharan

  • Uncovering shared structures in multiclass classification

    Yonatan Amit;Michael Fink;Nathan Srebro;Shimon Ullman

  • A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks

    Behnam Neyshabur;Srinadh Bhojanapalli;Nathan Srebro

  • SVM optimization: inverse dependence on training set size

    Shai Shalev-Shwartz;Nathan Srebro

  • Learning with matrix factorizations

    Nathan Srebro;Tommi S. Jaakkola

  • Global optimality of local search for low rank matrix recovery

    Srinadh Bhojanapalli;Behnam Neyshabur;Nathan Srebro

  • Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks

    Behnam Neyshabur;Zhiyuan Li;Srinadh Bhojanapalli;Yann LeCun

  • Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm

    Deanna Needell;Nathan Srebro;Rachel Ward

  • The Implicit Bias of Gradient Descent on Separable Data

    Daniel Soudry;Elad Hoffer;Mor Shpigel Nacson;Nathan Srebro

  • The Implicit Bias of Gradient Descent on Separable Data

    Daniel Soudry;Elad Hoffer;Nathan Srebro

Frequent Co-Authors

Behnam Neyshabur
Behnam Neyshabur New York University
Daniel Soudry
Daniel Soudry Technion – Israel Institute of Technology
Karthik Sridharan
Karthik Sridharan Cornell University
Ohad Shamir
Ohad Shamir Weizmann Institute of Science
Jason D. Lee
Jason D. Lee Princeton University
Ruslan Salakhutdinov
Ruslan Salakhutdinov Carnegie Mellon University
Shai Shalev-Shwartz
Shai Shalev-Shwartz Hebrew University of Jerusalem
Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign
Ryota Tomioka
Ryota Tomioka Microsoft (United States)

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