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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机器学习中多保真度超参数优化研究进展

有效地调优超参数对于提高机器学习模型的性能至关重要。然而，超参数优化（HPO）往往需要大量的计算预算，这通常是有限的。因此，在HPO中，有效地使用这种受限的预算是至关重要的。多保真HPO已经成为解决这一问题的潜在方法。本文对机器学习中的多保真度HPO进行了综述，讨论了HPO的最新算法，并提出了未来的研究方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.