Alejandro Morales-Hernández, Inneke Van Nieuwenhuyse, Gonzalo Nápoles
TL;DR本文介绍了一种使用Tree-structured Parzen Estimators采样策略和训练带异质噪声的Gaussian Process Regression元模型的多目标超参数优化方法，具有更好的超体积指标表现，并考虑到模型评估的不确定性。
Abstract
The performance of any machine learning (ML) algorithm is impacted by the choice of its hyperparameters. As training and evaluating a ML algorithm is usually expensive, the hyperparameter optimization (HPO) metho