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arXiv · 1603.06560

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

Lisha Li·Kevin Jamieson·Giulia DeSalvo·Afshin Rostamizadeh·Ameet Talwalkar
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Abstract

Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation and early-stopping. We formulate hyperparameter optimization as a pure-exploration non-stochastic infinite-armed bandit problem where a predefined resource like iterations, data samples, or features is allocated to randomly sampled configurations. We introduce a novel algorithm, Hyperband, for this framework and analyze its theoretical properties, providing several desirable guarantees. Furthermore, we compare Hyperband with popular Bayesian optimization methods on a suite of hyperparameter optimization problems. We observe that Hyperband can provide over an order-of-magnitude speedup over our competitor set on a variety of deep-learning and kernel-based learning problems.

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Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar. 2016-03-21. Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. https://arxiv.org/abs/1603.06560

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