Robust Bayesian Optimization

Robust Bayesian Optimization is a sequential decision-making framework that extends standard Bayesian optimization by explicitly incorporating uncertainty and variability in both the input parameters and the objective function evaluations, thereby seeking solutions that are robust to perturbations and noise in real-world implementation.

42

Publications

2.9K

Citations

146

Authors

72

Institutions

Publications per year

2017–2026

31

Authors

146

  1. FR
    Fabrizio Ruggeri

    Istituto di Matematica Applicata e Tecnologie Informatiche, Italy

    3 publications · 134 citations

    3

    h-index

  2. BK
    Bhavya Kailkhura

    Lawrence Livermore National Laboratory, United States

    2 publications · 72 citations

    2

    h-index

  3. IB
    Ilija Bogunovic

    École Polytechnique Fédérale de Lausanne, Switzerland

    2 publications · 27 citations

    2

    h-index

  4. MF
    Matthias Feurer

    University of Freiburg, Germany

    2 publications · 1.3K citations

    2

    h-index

  5. XL
    Xue Lin

    Northeastern University, United States

    2 publications · 72 citations

    2

    h-index

1–5 of 146

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 165 citations

    7

    h-index

  2. 2

    h-index

  3. Journal of Statistical Planning and Inference

    2 publications · 33 citations

    2

    h-index

  4. Neurocomputing

    1 publications · 91 citations

    1

    h-index

  5. Journal of Hydrology

    1 publications · 28 citations

    1

    h-index

1–5