Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
This addresses the critical problem of efficiently tuning hyperparameters for machine learning practitioners, offering a significant speed improvement over existing methods.
The paper tackles hyperparameter optimization by introducing Hyperband, a bandit-based algorithm that adaptively allocates resources and early-stops configurations, achieving over an order-of-magnitude speedup compared to Bayesian optimization methods on deep-learning and kernel-based problems.
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.