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.
@article{arxiv.1908.06756,
title = {BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters},
author = {Marius Lindauer and Katharina Eggensperger and Matthias Feurer and André Biedenkapp and Joshua Marben and Philipp Müller and Frank Hutter},
journal= {arXiv preprint arXiv:1908.06756},
year = {2019}
}