Heteroscedastic Bayesian optimization using generalized product of experts

Saulius Tautvaišas () and Julius Žilinskas ()
Additional contact information
Saulius Tautvaišas: Vilnius University
Julius Žilinskas: Vilnius University

Journal of Global Optimization, 2025, vol. 91, issue 2, No 8, 393-413

Abstract: Abstract In many real world optimization problems observations are corrupted by a heteroscedastic noise, which depends on the input location. Bayesian optimization (BO) is an efficient approach for global optimization of black-box functions, but the performance of using a Gaussian process (GP) model can degrade with changing levels of noise due to a homoscedastic noise assumption. However, a generalized product of experts (GPOE) model allows us to build independent GP experts on the subsets of observations with individual set of hyperparameters, which is flexible enough to capture the changing levels of noise. In this paper we propose a heteroscedastic Bayesian optimization algorithm by combining the GPOE model with two modifications of existing acquisition functions, which are capable of representing and penalizing heteroscedastic noise across the input space. We compare and evaluate the performance of GPOE based BO (GPOEBO) model on 6 synthetic global optimization functions corrupted with the heteroscedastic noise as well as on two real-world scientific datasets. The results show that GPOEBO is able to improve the accuracy compared to other methods.

Keywords: Heteroscedastic Bayesian optimization; Gaussian processes; Generalized product of experts; Augmented expected improvement (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10898-023-01333-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jglopt:v:91:y:2025:i:2:d:10.1007_s10898-023-01333-5

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/10898

DOI: 10.1007/s10898-023-01333-5

Access Statistics for this article

Journal of Global Optimization is currently edited by Sergiy Butenko

More articles in Journal of Global Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().