Publications Details

Publication Details

Towards Assessing the Impact of Bayesian Optimization’s Own Hyperparameters

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

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.

Machine Learning Section
External Organisation(s)
University of Freiburg
Bosch Center for Artificial Intelligence (BCAI)
Conference contribution
No. of pages
Publication date
Publication status
E-pub ahead of print
Peer reviewed
Electronic version(s) (Access: Open)