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Multi-objective hyperparameter optimization with performance uncertainty

Published in arXiv (Cornell University) • Sep 9, 2022
NobleIDNI3P07W41R32S96
Authors:
Alejandro Morales-Hernández
,
Inneke Van Nieuwenhuyse
,
Gonzalo Nápoles

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) method needs to be computationally efficient to be useful in practice. Most of the existing a...

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