  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.
