Scalable Bayesian optimization with generalized product of experts

Bayesian optimization (BO) is challenging for problems with large number of observations. The main limitation of the Gaussian Process (GP) based BO is the computational cost which grows cubically with the number of sample points. To alleviate scalability issues of standard GP we propose to use the g...

Full description

Saved in:
Bibliographic Details
Published inJournal of global optimization Vol. 88; no. 3; pp. 777 - 802
Main Authors Tautvaisas, Saulius, Zilinskas, Julius
Format Journal Article
LanguageEnglish
Published New York Springer US 01.03.2024
Springer
Springer Nature B.V
Subjects
Algorithms
Approximation
Bayesian analysis
Computer Science
Gaussian process
Global optimization
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Random variables
Real Functions
Bayesian Optimization
Gaussian Processes
Global Black-Box Optimization
Generalized Product of Experts
Online AccessGet full text

Cover

More Information
Summary:Bayesian optimization (BO) is challenging for problems with large number of observations. The main limitation of the Gaussian Process (GP) based BO is the computational cost which grows cubically with the number of sample points. To alleviate scalability issues of standard GP we propose to use the generalized product of experts (gPoE) model. This model is not only very flexible and scalable but can be efficiently computed in parallel. Moreover, we propose a new algorithm gPoETRBO for global optimization with large number of observations which combines trust region and gPoE models. In our experiments, we empirically show that our proposed algorithms are computationally more efficient and achieve similar performance to other state-of-the-art algorithms without using any specialized hardware.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-022-01236-x