World's Best Scientists 2026 revealed!
Olivier Cappé

Olivier Cappé

D-Index & Metrics

Computer Science

D-Index
50
Citations
14446
World Ranking
5501
National Ranking
115

Olivier Cappé 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 Olivier Cappé 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: 135 publications — 21st percentile

21% of scientists in this discipline score the same or lower.

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

Olivier Cappé 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 Olivier Cappé 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: 50 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Overview

Olivier Cappé is affiliated with PSL University in France and has contributed extensively to research in decision sciences and computer science. Their work spans a range of topics within these fields, with a particular focus on management science and operations research, artificial intelligence, statistics and probability, signal processing, and marketing.

Their main research topics include advanced bandit algorithms, machine learning and algorithms, auction theory and applications, consumer market behavior and pricing, optimization and search problems, statistical methods in clinical trials, and advanced statistical process monitoring.

Olivier Cappé's recent publications reflect a concentration on algorithmic and statistical approaches to decision-making and data analysis, especially in adaptive and non-stationary environments. Notable papers include:

  • "Algorithms for Non-Stationary Generalized Linear Bandits" (2020, arXiv (Cornell University))
  • "A/B/n Testing with Control in the Presence of Subpopulations" (2021, arXiv (Cornell University))
  • "A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix Factorization" (2021, IEEE Transactions on Signal Processing)
  • "Efficient Algorithms for Stochastic Repeated Second-price Auctions" (2020, HAL (Le Centre pour la Communication Scientifique Directe))
  • "Self-Concordant Analysis of Generalized Linear Bandits with Forgetting" (2020, arXiv (Cornell University))

Their frequent collaborators include Aurélien Garivier, Yoan Russac, Juliette Achddou, Christina Katsimerou, and Dennis Bohle.

Olivier Cappé publishes primarily in venues such as arXiv (Cornell University), IEEE Transactions on Signal Processing, and HAL (Le Centre pour la Communication Scientifique Directe). These platforms reflect their engagement with both open-access preprint repositories and peer-reviewed journals within engineering and signal processing communities.

Best Publications

  • Continuous probabilistic transform for voice conversion

    Y. Stylianou;O. Cappe;E. Moulines

  • An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo

    O. Cappe;S.J. Godsill;E. Moulines

  • Comparison of resampling schemes for particle filtering

    R. Douc;O. Cappe

  • Elimination of the musical noise phenomenon with the Ephraim and Malah noise suppressor

    O. Cappe

  • On the complexity of best-arm identification in multi-armed bandit models

    Emilie Kaufmann;Olivier Cappé;Aurélien Garivier

  • Population Monte Carlo

    Olivier Cappé;Arnaud Guillin;Jean-Michel Marin;Christian P. Robert

  • On‐line expectation–maximization algorithm for latent data models

    Olivier Cappé;Eric Moulines

  • The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond

    Aurélien Garivier;Olivier Cappé

  • Digital Audio Restoration

    Simon Godsill;Peter Rayner;Olivier Cappé

  • Practical Very Large Scale CRFs

    Thomas Lavergne;Olivier Cappé;François Yvon

  • Inference in Hidden Markov Models (Springer Series in Statistics)

    Olivier Cappé;Eric Moulines;Tobias Ryden

  • Kullback–Leibler upper confidence bounds for optimal sequential allocation

    Olivier Cappé;Aurélien Garivier;Odalric-Ambrym Maillard;Rémi Munos

  • Parametric Bandits: The Generalized Linear Case

    Sarah Filippi;Olivier Cappe;Aurélien Garivier;Csaba Szepesvári

  • On Bayesian Upper Confidence Bounds for Bandit Problems

    Emilie Kaufmann;Olivier Cappé;Aurélien Garivier

  • Adaptive importance sampling in general mixture classes

    Olivier Cappé;Randal Douc;Arnaud Guillin;Jean-Michel Marin

  • Comparison of Resampling Schemes for Particle Filtering

    Randal Douc;Olivier Cappé;Eric Moulines

  • Sequential Monte Carlo smoothing with application to parameter estimation in nonlinear state space models

    Jimmy Olsson;Olivier Cappé;Randal Douc;Éric Moulines

  • Online EM Algorithm for Hidden Markov Models

    Olivier Cappé

  • Long-range dependence and heavy-tail modeling for teletraffic data

    O. Cappe;E. Moulines;J.-C. Pesquet;A.P. Petropulu

  • Reversible jump, birth‐and‐death and more general continuous time Markov chain Monte Carlo samplers

    Olivier Cappé;Christian P. Robert;Tobias Rydén

  • Inference in Hid-den Markov Models

    Olivier Cappe;Eric Moulines;Tobias Ryden

Frequent Co-Authors

Eric Moulines
Eric Moulines Mohamed bin Zayed University of Artificial Intelligence
Christian P. Robert
Christian P. Robert Paris Dauphine University
Simon Prunet
Simon Prunet Observatoire de la Côte d’Azur
Jean-Michel Marin
Jean-Michel Marin University of Montpellier
Cédric Févotte
Cédric Févotte Toulouse Institute of Computer Science Research
Arnaud Guillin
Arnaud Guillin University of Clermont Auvergne
Simon J. Godsill
Simon J. Godsill University of Cambridge
Yannis Stylianou
Yannis Stylianou University of Crete
Zaid Harchaoui
Zaid Harchaoui University of Washington
François R. Bouchet
François R. Bouchet Institut d'Astrophysique de Paris

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