Stochastic Bandits

Stochastic Bandits is a sequential decision-making framework in which a learner repeatedly selects actions from a fixed set of alternatives and receives noisy reward feedback, with the defining characteristic that the rewards associated with each action are drawn independently from fixed but unknown probability distributions.

618

Publications

44.3K

Citations

940

Authors

373

Institutions

Publications per year

2017–2026

251

Variants

Stochastic Multi-armed Bandits

Authors

940

  1. CS
    Csaba Szepesvári

    University of Alberta, Canada

    24 publications · 2.2K citations

    19

    h-index

  2. RM
    Rémi Munos

    Google (United States), United States

    16 publications · 1.5K citations

    15

    h-index

  3. BK
    Branislav Kveton

    Adobe Systems (United States), United States

    14 publications · 645 citations

    12

    h-index

  4. QZ
    Qing Zhao

    University of California, Davis, United States

    12 publications · 1.3K citations

    12

    h-index

  5. PA
    Peter Auer

    Montanuniversität Leoben, Austria

    12 publications · 3.1K citations

    11

    h-index

1–5 of 940

Venues

  1. arXiv (Cornell University)

    Repository · 166 publications · 7K citations

    42

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 14 publications · 419 citations

    13

    h-index

  3. IEEE Transactions on Automatic Control

    13 publications · 1.3K citations

    12

    h-index

  4. Advances in Applied Probability

    AAP · 14 publications · 1.2K citations

    12

    h-index

  5. Mathematics of Operations Research

    15 publications · 1.5K citations

    12

    h-index

1–5