Recent advances in Bayesian optimization

X Wang, Y Jin, S Schmitt, M Olhofer - ACM Computing Surveys, 2023 - dl.acm.org
Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Bayesian optimization for adaptive experimental design: A review

S Greenhill, S Rana, S Gupta, P Vellanki… - IEEE …, 2020 - ieeexplore.ieee.org
Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …

[图书][B] Surrogates: Gaussian process modeling, design, and optimization for the applied sciences

RB Gramacy - 2020 - taylorfrancis.com
Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

Derivative-free optimization methods

J Larson, M Menickelly, SM Wild - Acta Numerica, 2019 - cambridge.org
In many optimization problems arising from scientific, engineering and artificial intelligence
applications, objective and constraint functions are available only as the output of a black …

A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization

M Binois, N Wycoff - ACM Transactions on Evolutionary Learning and …, 2022 - dl.acm.org
Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …

Constrained Bayesian optimization with noisy experiments

B Letham, B Karrer, G Ottoni, E Bakshy - 2019 - projecteuclid.org
Constrained Bayesian Optimization with Noisy Experiments Page 1 Bayesian Analysis (2019)
14, Number 2, pp. 495–519 Constrained Bayesian Optimization with Noisy Experiments …

A framework for Bayesian optimization in embedded subspaces

A Nayebi, A Munteanu… - … Conference on Machine …, 2019 - proceedings.mlr.press
We present a theoretically founded approach for high-dimensional Bayesian optimization
based on low-dimensional subspace embeddings. We prove that the error in the Gaussian …

Scalable constrained Bayesian optimization

D Eriksson, M Poloczek - International Conference on …, 2021 - proceedings.mlr.press
The global optimization of a high-dimensional black-box function under black-box
constraints is a pervasive task in machine learning, control, and engineering. These …

Active learning for deep Gaussian process surrogates

A Sauer, RB Gramacy, D Higdon - Technometrics, 2023 - Taylor & Francis
Abstract Deep Gaussian processes (DGPs) are increasingly popular as predictive models in
machine learning for their nonstationary flexibility and ability to cope with abrupt regime …

Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence

N Baker, F Alexander, T Bremer, A Hagberg… - 2019 - osti.gov
Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …