Mind the Gap: Measuring Generalization Performance Across Multiple Objectives

MCML Authors

Matthias Feurer

Prof. Dr.

Thomas Bayes Fellow

* Former Thomas Bayes Fellow

Florian Pfisterer

Dr.

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Abstract

Modern machine learning models are often constructed taking into account multiple objectives, e.g., minimizing inference time while also maximizing accuracy. Multi-objective hyperparameter optimization (MHPO) algorithms return such candidate models, and the approximation of the Pareto front is used to assess their performance. In practice, we also want to measure generalization when moving from the validation to the test set. However, some of the models might no longer be Pareto-optimal which makes it unclear how to quantify the performance of the MHPO method when evaluated on the test set. To resolve this, we provide a novel evaluation protocol that allows measuring the generalization performance of MHPO methods and studying its capabilities for comparing two optimization experiments.

inproceedings

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IDA 2023

21st International Symposium on Intelligent Data Analysis. Louvain-la-Neuve, Belgium, Apr 12-14, 2023.

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Authors

M. Feurer • K. Eggensperger • E. Bergman • F. Pfisterer • B. Bischl • F. Hutter

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability