World's Best Scientists 2026 revealed!
Simon Lacoste-Julien

Simon Lacoste-Julien

D-Index & Metrics

Computer Science

D-Index
45
Citations
8513
World Ranking
7191
National Ranking
286

Simon Lacoste-Julien 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 Simon Lacoste-Julien 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: 116 publications — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Simon Lacoste-Julien 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 Simon Lacoste-Julien 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Simon Lacoste-Julien is affiliated with the University of Montreal in Canada, focusing primarily on research within the field of Computer Science. Their work spans across several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Statistics and Probability, and Computational Theory and Mathematics.

The scientist has contributed extensively to topics such as Domain Adaptation and Few-Shot Learning, Stochastic Gradient Optimization Techniques, Advanced Neural Network Applications, Machine Learning and Algorithms, Sparse and Compressive Sensing Techniques, Neural Networks and Applications, and Markov Chains and Monte Carlo Methods.

Recent publications by Simon Lacoste-Julien include:

  • A Survey of Self-Supervised and Few-Shot Object Detection, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence, 2020, arXiv (Cornell University)
  • Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information, 2022, arXiv (Cornell University)
  • Bayesian Structure Learning with Generative Flow Networks, 2022, arXiv (Cornell University)
  • Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information, 2021, INFORMS journal on computing

Lacoste-Julien's work has been published frequently in the venue arXiv (Cornell University), with 51 publications, alongside contributions to the IEEE Transactions on Pattern Analysis and Machine Intelligence, INFORMS journal on computing, Machine Learning, and EURO Journal on Transportation and Logistics.

Collaboration appears as a significant aspect of this researcher's output, with frequent coauthors including Sébastien Lachapelle, Gauthier Gidel, Ioannis Mitliagkas, Yoshua Bengio, and Nicolas Loizou, reflecting ongoing partnerships within their research network.

Best Publications

  • SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives

    Aaron Defazio;Francis Bach;Simon Lacoste-Julien

  • A closer look at memorization in deep networks

    Devansh Arpit;Stanisław Jastrzębski;Nicolas Ballas;David Krueger

  • DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification

    Simon Lacoste-Julien;Fei Sha;Michael I. Jordan

  • Block-Coordinate Frank-Wolfe Optimization for Structural SVMs

    Simon Lacoste-Julien;Martin Jaggi;Mark Schmidt;Patrick Pletscher

  • On the global linear convergence of Frank-Wolfe optimization variants

    Simon Lacoste-Julien;Martin Jaggi

  • Unsupervised Learning from Narrated Instruction Videos

    Jean-Baptiste Alayrac;Piotr Bojanowski;Nishant Agrawal;Nishant Agrawal;Josef Sivic

  • A Discriminative Matching Approach to Word Alignment

    Ben Taskar;Lacoste-Julien Simon;Klein Dan

  • A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method

    Simon Lacoste-Julien;Mark W. Schmidt;Francis R. Bach

  • A Variational Inequality Perspective on Generative Adversarial Networks

    Gauthier Gidel;Hugo Berard;Gaëtan Vignoud;Pascal Vincent

  • SIGMa: simple greedy matching for aligning large knowledge bases

    Simon Lacoste-Julien;Konstantina Palla;Alex Davies;Gjergji Kasneci

  • On pairwise costs for network flow multi-object tracking

    Visesh Chari;Simon Lacoste-Julien;Ivan Laptev;Josef Sivic

  • Structured Prediction, Dual Extragradient and Bregman Projections

    Ben Taskar;Simon Lacoste-Julien;Michael I. Jordan

  • Convergence Rate of Frank-Wolfe for Non-Convex Objectives.

    Simon Lacoste-Julien;Simon Lacoste-Julien

  • On the Equivalence between Herding and Conditional Gradient Algorithms

    Simon Lacoste-julien;Francis R. Bach;Guillaume R. Obozinski

  • On the Equivalence between Herding and Conditional Gradient Algorithms

    Francis Bach;Simon Lacoste-Julien;Guillaume Obozinski

  • Negative Momentum for Improved Game Dynamics

    Gauthier Gidel;Reyhane Askari Hemmat;Mohammad Pezeshki;Gabriel Huang

  • PAC-Bayesian theory meets Bayesian inference

    Pascal Germain;Francis Bach;Alexandre Lacoste;Simon Lacoste-Julien

  • Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates

    Sharan Vaswani;Aaron Mishkin;Issam H. Laradji;Mark Schmidt

  • Variance reduced stochastic gradient descent with neighbors

    Thomas Hofmann;Aurelien Lucchi;Simon Lacoste-Julien;Brian McWilliams

  • ASAGA: Asynchronous Parallel SAGA

    Rémi Leblond;Fabian Pedregosa;Simon Lacoste-Julien

  • A Modern Take on the Bias-Variance Tradeoff in Neural Networks

    Brady Neal;Sarthak Mittal;Aristide Baratin;Vinayak Tantia

  • Differentiable Causal Discovery from Interventional Data

    Philippe Brouillard;Sébastien Lachapelle;Alexandre Lacoste;Simon Lacoste-Julien

Frequent Co-Authors

Francis Bach
Francis Bach École Normale Supérieure
Pascal Vincent
Pascal Vincent Facebook (United States)
Ivan Laptev
Ivan Laptev Mohamed bin Zayed University of Artificial Intelligence
Josef Sivic
Josef Sivic Czech Technical University in Prague
Mark Schmidt
Mark Schmidt University of British Columbia
Yoshua Bengio
Yoshua Bengio University of Montreal
François Soumis
François Soumis Polytechnique Montréal
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Piotr Bojanowski
Piotr Bojanowski Facebook (United States)
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge

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