Recent advances in Bayesian optimization
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 …
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
Bayesian optimization for adaptive experimental design: A review
Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …
“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 …
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
Derivative-free optimization methods
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 …
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
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 …
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
Constrained Bayesian optimization with noisy experiments
Constrained Bayesian Optimization with Noisy Experiments Page 1 Bayesian Analysis (2019)
14, Number 2, pp. 495–519 Constrained Bayesian Optimization with Noisy Experiments …
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 …
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 …
constraints is a pervasive task in machine learning, control, and engineering. These …
Active learning for deep Gaussian process surrogates
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 …
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
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 …
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …