Bayesian Optimization Under Uncertainty

Bayesian Optimization Under Uncertainty is a mathematical optimization framework that integrates Bayesian inference to sequentially select and evaluate candidate solutions under uncertain objective functions, distinguished by its use of probabilistic surrogate models to balance exploration and exploitation while quantifying uncertainty in predictions.

527

Publications

28.2K

Citations

1.7K

Authors

630

Institutions

Publications per year

2017–2026

383

Authors

1.7K

  1. SR
    Santu Rana

    Deakin University, Australia

    9 publications · 243 citations

    9

    h-index

  2. SV
    Svetha Venkatesh

    Deakin University, Australia

    9 publications · 243 citations

    9

    h-index

  3. SG
    Sunil Gupta

    Deakin University, Australia

    8 publications · 226 citations

    8

    h-index

  4. PI
    Peter I. Frazier

    Cornell University, United States

    8 publications · 782 citations

    8

    h-index

  5. AK
    Andreas Krause

    ETH Zurich, Switzerland

    7 publications · 215 citations

    7

    h-index

1–5 of 1.7K

Venues

  1. arXiv (Cornell University)

    Repository · 46 publications · 4K citations

    22

    h-index

  2. 7

    h-index

  3. Expert Systems with Applications

    6 publications · 526 citations

    6

    h-index

  4. Structural and Multidisciplinary Optimization

    6 publications · 232 citations

    6

    h-index

  5. Journal of Mechanical Design

    5 publications · 143 citations

    5

    h-index

1–5