Multi-armed Bandit Optimization

Multi-Armed Bandit Optimization is a sequential decision-making framework in personalization analytics that dynamically balances exploration and exploitation to maximize cumulative rewards by allocating traffic or resources across competing alternatives based on observed performance outcomes.

591

Publications

31.7K

Citations

1.3K

Authors

503

Institutions

Publications per year

2017–2026

303

Authors

1.3K

  1. SH
    Sherief Hashima

    Egyptian Atomic Energy Authority, Egypt

    11 publications · 379 citations

    11

    h-index

  2. EM
    Ehab Mahmoud Mohamed

    Aswan University, Egypt

    11 publications · 396 citations

    11

    h-index

  3. KH
    Kohei Hatano

    Kyushu University, Japan

    11 publications · 379 citations

    11

    h-index

  4. BK
    Bhaskar Krishnamachari

    University of Southern California, United States

    11 publications · 1K citations

    10

    h-index

  5. ML
    Mingyan Liu

    University of Michigan, United States

    9 publications · 236 citations

    9

    h-index

1–5 of 1.3K

Venues

  1. arXiv (Cornell University)

    Repository · 72 publications · 2.1K citations

    25

    h-index

  2. IEEE Transactions on Wireless Communications

    18 publications · 817 citations

    17

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 14 publications · 720 citations

    13

    h-index

  4. IEEE Transactions on Vehicular Technology

    13 publications · 786 citations

    12

    h-index

  5. 9

    h-index

1–5