Regret Optimization

Regret Optimization is a decision-theoretic framework within risk optimization that minimizes the discrepancy between the performance of chosen actions and the best possible actions in hindsight under uncertainty.

680

Publications

32.7K

Citations

1.3K

Authors

506

Institutions

Publications per year

2017–2026

293

Authors

1.3K

  1. CS
    Csaba Szepesvári

    University of Alberta, Canada

    18 publications · 749 citations

    16

    h-index

  2. EH
    Elad Hazan

    Princeton University, United States

    17 publications · 879 citations

    16

    h-index

  3. YM
    Yishay Mansour

    Tel Aviv University, Israel

    15 publications · 478 citations

    12

    h-index

  4. ZW
    Zheng Wen

    Adobe Systems (United States), United States

    11 publications · 486 citations

    11

    h-index

  5. TL
    Tor Lattimore

    University of Alberta, Canada

    11 publications · 433 citations

    11

    h-index

1–5 of 1.3K

Venues

  1. arXiv (Cornell University)

    Repository · 150 publications · 6.2K citations

    43

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 16 publications · 735 citations

    15

    h-index

  3. European Journal of Operational Research

    12 publications · 987 citations

    12

    h-index

  4. Neural Information Processing Systems

    Conference · 13 publications · 392 citations

    11

    h-index

  5. SSRN Electronic Journal

    Repository · 11 publications · 381 citations

    10

    h-index

1–5