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BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters (1908.06756v1)
Published 16 Aug 2019 in cs.LG, cs.AI, and stat.ML
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.
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