Can I read BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters on EtoBox?

BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters by Lindauer, Marius; Eggensperger, Katharina; Feurer, Matthias; Biedenkapp, André; Marben, Joshua; Müller, Philipp; Hutter, Frank is a scholarly article available to read on EtoBox.

What is BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters about?

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.

Author

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

Published

2019

Language

EN