Multi-objective hyperparameter optimization with performance uncertainty

• URL: http://arxiv.org/abs/2209.04340v1
• Date: Fri, 9 Sep 2022 14:58:43 GMT
• Title: Multi-objective hyperparameter optimization with performance uncertainty
• Authors: Alejandro Morales-Hern\'andez and Inneke Van Nieuwenhuyse and Gonzalo N\'apoles
• Abstract summary: This paper presents results on multi-objective hyperparameter optimization with uncertainty on the evaluation of Machine Learning algorithms. We combine the sampling strategy of Tree-structured Parzen Estimators (TPE) with the metamodel obtained after training a Gaussian Process Regression (GPR) with heterogeneous noise. Experimental results on three analytical test functions and three ML problems show the improvement over multi-objective TPE and GPR.
• Score: 62.997667081978825
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 approaches on multi-objective HPO use evolutionary strategies and metamodel-based optimization. However, few methods have been developed to account for uncertainty in the performance measurements. This paper presents results on multi-objective hyperparameter optimization with uncertainty on the evaluation of ML algorithms. We combine the sampling strategy of Tree-structured Parzen Estimators (TPE) with the metamodel obtained after training a Gaussian Process Regression (GPR) with heterogeneous noise. Experimental results on three analytical test functions and three ML problems show the improvement over multi-objective TPE and GPR, achieved with respect to the hypervolume indicator.

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