Active Learning For Optimization

Active Learning for Optimization is a simulation-based optimization strategy that iteratively selects informative queries to guide the search for optimal solutions, distinguishing itself by balancing exploration and exploitation through uncertainty-aware sampling to reduce computational effort in expensive black-box optimization.

306

Publications

23.9K

Citations

1K

Authors

391

Institutions

Publications per year

2017–2026

188

Authors

1K

  1. AK
    Andreas Krause

    ETH Zurich, Switzerland

    6 publications · 863 citations

    6

    h-index

  2. JÁ
    J. Ángel Velázquez‐Iturbide

    Universidad Rey Juan Carlos, Spain

    4 publications · 86 citations

    4

    h-index

  3. LG
    Liang Gao

    Huazhong University of Science and Technology, China

    4 publications · 389 citations

    4

    h-index

  4. TL
    Turab Lookman

    Los Alamos National Laboratory, United States

    4 publications · 801 citations

    4

    h-index

  5. KM
    Kyoungmin Min

    Soongsil University, South Korea

    3 publications · 61 citations

    3

    h-index

1–5 of 1K

Venues

  1. Reliability Engineering & System Safety

    10 publications · 386 citations

    10

    h-index

  2. arXiv (Cornell University)

    Repository · 11 publications · 514 citations

    10

    h-index

  3. Neurocomputing

    9 publications · 351 citations

    9

    h-index

  4. Structural Safety

    6 publications · 2.7K citations

    6

    h-index

  5. Pattern Recognition

    6 publications · 190 citations

    6

    h-index

1–5