BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters

authored by

Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Joshua Marben, Philipp Müller, Frank Hutter

Abstract

Hyperparameter optimization and neural architecture search can become prohibitively expensive for regular black-box Bayesian optimization because the training and evaluation of a single model can easily take several hours. To overcome this, we introduce a comprehensive tool suite for effective multi-fidelity Bayesian optimization and the analysis of its runs. The suite, written in Python, provides a simple way to specify complex design spaces, a robust and efficient combination of Bayesian optimization and HyperBand, and a comprehensive analysis of the optimization process and its outcomes.

External Organisation(s)

University of Freiburg
Robert Bosch GmbH

Type

Preprint

Publication date

16.08.2019

Publication status

E-pub ahead of print

Electronic version(s)

https://arxiv.org/pdf/1908.06756 (Access: Unknown)