A flexible framework for hyperparameter optimization using homotopy and surrogate models

Abstract Over the past few decades, machine learning has made remarkable strides, owed largely to algorithmic advancements and the abundance of high-quality, large-scale datasets. However, an equally crucial aspect in achieving optimal model performance is the fine-tuning of hyperparameters. Despite...

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Bibliografiske detaljer

Principais autores:	Sophia J. Abraham, Kehelwala D. G. Maduranga, Jeffery Kinnison, Zachariah Carmichael, Jonathan D. Hauenstein, Walter J. Scheirer
Format:	Artigo
Sprog:	Inglês
Udgivet:	Nature Portfolio 2026-02-01
Serier:	Scientific Reports
Fag:	Hyperparameter Optimization (HPO) Homotopy Methods Surrogate Models Convergence Efficiency Optimization Benchmarks Machine Learning Models
Online adgang:	https://doi.org/10.1038/s41598-026-39713-y
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