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
527
Eric Brochu, Vlad M. Cora, Nando de Freitas · arXiv (Cornell University) · 2010 · 2.2K citations · Full text
A Tutorial on Bayesian Optimization
Peter I. Frazier · arXiv (Cornell University) · 2018 · 376 citations · Full text
1–5 of 527
1.7K
9
h-index
9
h-index
8
h-index
8
h-index
7
h-index
1–5 of 1.7K
630
ETH Zurich
Zurich, Switzerland
12 h-index
42 publications
1.4K citations
Politecnico di Milano
Milan, Italy
10 h-index
20 publications
736 citations
University College London
London, United Kingdom
10 h-index
18 publications
600 citations
Massachusetts Institute of Technology
Cambridge, United States
10 h-index
26 publications
1.2K citations

Imperial College London
London, United Kingdom
10 h-index
22 publications
1.4K citations

Cornell University
Ithaca, United States
10 h-index
15 publications
714 citations
Georgia Institute of Technology
Atlanta, United States
9 h-index
28 publications
741 citations
University of California, Berkeley
Berkeley, United States
7 h-index
20 publications
750 citations
1–8 of 630
Repository · 46 publications · 4K citations
22
h-index
7 publications · 300 citations
7
h-index
6 publications · 526 citations
6
h-index
6 publications · 232 citations
6
h-index
5 publications · 143 citations
5
h-index
1–5