A review on multi-fidelity hyperparameter optimization in machine learning

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express Pub Date : 2025-02-28 DOI:10.1016/j.icte.2025.02.008

Jonghyeon Won , Hyun-Suk Lee , Jang-Won Lee

引用次数: 0

Abstract

Tuning hyperparameters effectively is crucial for improving the performance of machine learning models. However, hyperparameter optimization (HPO) often demands significant computational budget, which is typically limited. Therefore, efficiently using this constrained budget is critical in HPO. Multi-fidelity HPO has emerged as a potential solution to this issue. This paper presents a comprehensive review of multi-fidelity HPO in machine learning, discusses recent algorithms for HPO, and proposes directions for future research.

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来源期刊

ICT Express Multiple-

CiteScore

10.20

自引率

1.90%

发文量

167

审稿时长

35 weeks

期刊介绍： The ICT Express journal published by the Korean Institute of Communications and Information Sciences (KICS) is an international, peer-reviewed research publication covering all aspects of information and communication technology. The journal aims to publish research that helps advance the theoretical and practical understanding of ICT convergence, platform technologies, communication networks, and device technologies. The technology advancement in information and communication technology (ICT) sector enables portable devices to be always connected while supporting high data rate, resulting in the recent popularity of smartphones that have a considerable impact in economic and social development.