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
Parents
42
Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger et al. · Neural Information Processing Systems · 2015 · 1.3K citations
Bayesian Optimization of a Laser-Plasma Accelerator
Sören Jalas, Manuel Kirchen, Philipp Messner et al. · Physical Review Letters · 2021 · 137 citations · Full text
1–5 of 42
146
Istituto di Matematica Applicata e Tecnologie Informatiche, Italy
3 publications · 134 citations
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2
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2
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2
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1–5 of 146
72

University of Waterloo
Waterloo, Canada
3 h-index
5 publications
146 citations
University of California, Berkeley
Berkeley, United States
2 h-index
3 publications
105 citations
Duke University
Durham, United States
2 h-index
2 publications
69 citations
Decision Sciences (United States)
Fort Walton Beach, United States
2 h-index
2 publications
69 citations
Nanjing University of Science and Technology
Nanjing, China
2 h-index
3 publications
58 citations

University of Calgary
Calgary, Canada
1 h-index
1 publications
14 citations
University of Lausanne
Lausanne, Switzerland
1 h-index
1 publications
30 citations

Indian Statistical Institute
Kolkata, India
1 h-index
1 publications
21 citations
1–8 of 72
Repository · 7 publications · 165 citations
7
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2 publications · 69 citations
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2 publications · 33 citations
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1 publications · 91 citations
1
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1 publications · 28 citations
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