Advancing Hyperparameter Optimization: Foundations, Multiple Objectives and Algorithmic Innovations Informed Through Benchmarking

MCML Authors

Lennart Schneider

→ Group Bernd Bischl
 Statistical Learning and Data Science

Abstract

Hyperparameter optimization (HPO) is a fundamental aspect of machine learning (ML), directly influencing model performance and adaptability. As a computationally expensive black-box optimization problem, HPO requires efficient algorithms to identify optimal hyperparameter configurations. This thesis advances the field of HPO along three key dimensions: foundational insights, HPO in the presence of more than one objective, and algorithmic innovations through benchmarking. (Shortened.)

phdthesis

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Dissertation

LMU München. May. 2025

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Authors

L. Schneider

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability