Unveiling thePower ofBayesian Optimization: Methods, Insights, andApplications

Title

Unveiling thePower ofBayesian Optimization: Methods, Insights, andApplications

Creator

Narayanan, Prithika

Description

Bayesian optimization (BO) has emerged as a popular approach for optimizing expensive black-box functions, which are common in modern machine learning, scientific research, and industrial design. This paper provides a comprehensive review of the recent advances in Bayesian optimization techniques, addressing new methodological developments such as multi-fidelity optimization, transfer learning, and neural network surrogates. Additionally, we explore the increasing role of BO in complex, high-dimensional, and multi-objective optimization problems, as well as its application in various fields like hyperparameter tuning, reinforcement learning, and neural architecture search. The goal of this review is to offer both theoretical insights and practical guidelines to researchers and practitioners working in areas where BO is a suitable tool. Finally, we discuss key challenges and propose directions for future research in the rapidly evolving field of Bayesian optimization. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

Source

Lecture Notes in Networks and Systems;Volume;1638 LNNS;pp.525-552

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Academic performance; Cultural factors; Economic factors; Social factors; Structural equation model

Coverage

Narayanan P., Department of Statistics and Data Science, Christ University, Karnataka, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981953308-4;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-95-3309-1_35

https://www.scopus.com/pages/publications/105036702391?origin=resultslist