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 …
A tutorial on Bayesian optimization
PI Frazier - arXiv preprint arXiv:1807.02811, 2018 - arxiv.org
Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …
State-of-the-art and comparative review of adaptive sampling methods for kriging
Metamodels aim to approximate characteristics of functions or systems from the knowledge
extracted on only a finite number of samples. In recent years kriging has emerged as a …
extracted on only a finite number of samples. In recent years kriging has emerged as a …
Bayesian optimization
PI Frazier - Recent advances in optimization and modeling …, 2018 - pubsonline.informs.org
Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …
Expected improvement for expensive optimization: a review
The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
Review of multi-fidelity models
MG Fernández-Godino - arXiv preprint arXiv:1609.07196, 2016 - arxiv.org
This article provides an overview of multi-fidelity modeling trends. Fidelity in modeling refers
to the level of detail and accuracy provided by a predictive model or simulation. Generally …
to the level of detail and accuracy provided by a predictive model or simulation. Generally …
Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly
K Kandasamy, KR Vysyaraju, W Neiswanger… - Journal of Machine …, 2020 - jmlr.org
Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of
expensive black box functions, which use introspective Bayesian models of the function to …
expensive black box functions, which use introspective Bayesian models of the function to …
Remarks on multi-output Gaussian process regression
Multi-output regression problems have extensively arisen in modern engineering
community. This article investigates the state-of-the-art multi-output Gaussian processes …
community. This article investigates the state-of-the-art multi-output Gaussian processes …
Review of surrogate modeling in water resources
Surrogate modeling, also called metamodeling, has evolved and been extensively used
over the past decades. A wide variety of methods and tools have been introduced for …
over the past decades. A wide variety of methods and tools have been introduced for …
[图书][B] Surrogate-model-based design and optimization
Surrogate-Model-Based Design and Optimization | SpringerLink Skip to main content
Advertisement SpringerLink Account Menu Find a journal Publish with us Track your research …
Advertisement SpringerLink Account Menu Find a journal Publish with us Track your research …