Practical Hyperparameter Optimization for Deep LearningDownload PDF

12 Feb 2018 (modified: 05 May 2023)ICLR 2018 Workshop SubmissionReaders: Everyone
Abstract: Recently, the bandit-based strategy Hyperband (HB) was shown to yield good hyperparameter settings of deep neural networks faster than vanilla Bayesian optimization (BO). However, for larger budgets, HB is limited by its random search component, and BO works better. We propose to combine the benefits of both approaches to obtain a new practical state-of-the-art hyperparameter optimization method, which we show to consistently outperform both HB and BO on a range of problem types, including feed-forward neural networks, Bayesian neural networks, and deep reinforcement learning. Our method is robust and versatile, while at the same time being conceptually simple and easy to implement.
Keywords: Hyperparameter optimization, Bayesian optimization
TL;DR: We combine Bayesian optimization and Hyperband to obtain a practical hyperparameter optimizer that consistently outperforms both of them.
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