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)