Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Narayanan, Prithika Title A name given to the resource Unveiling thePower ofBayesian Optimization: Methods, Insights, andApplications Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1638 LNNS;pp.525-552 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-3309-1_35" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-3309-1_35</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036702391?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036702391?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Narayanan P., Department of Statistics and Data Science, Christ University, Karnataka, Bengaluru, India Description An account of the resource 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. Subject The topic of the resource Academic performance; Cultural factors; Economic factors; Social factors; Structural equation model Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981953308-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online