Towards Assessing the Impact of Bayesian Optimization’s Own Hyperparameters

verfasst von

Marius Lindauer, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Frank Hutter

Abstract

Bayesian Optimization (BO) is a common approach for hyperparameter optimization (HPO) in automated machine learning. Although it is well-accepted that HPO is crucial to obtain well-performing machine learning models, tuning BO's own hyperparameters is often neglected. In this paper, we empirically study the impact of optimizing BO's own hyperparameters and the transferability of the found settings using a wide range of benchmarks, including artificial functions, HPO and HPO combined with neural architecture search. In particular, we show (i) that tuning can improve the any-time performance of different BO approaches, that optimized BO settings also perform well (ii) on similar problems and (iii) partially even on problems from other problem families, and (iv) which BO hyperparameters are most important.

Externe Organisation(en)

Albert-Ludwigs-Universität Freiburg
Bosch Center for Artificial Intelligence (BCAI)

Typ

Aufsatz in Konferenzband

Anzahl der Seiten

8

Publikationsdatum

2019

Publikationsstatus

Elektronisch veröffentlicht (E-Pub)

Peer-reviewed

Ja

Elektronische Version(en)

https://arxiv.org/abs/1908.06674 (Zugang: Offen)