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Bayesian optimization has become a successful tool for hyperparameteroptimization of machine learning algorithms, such as support vector machines ordeep neural networks. Despite its success, for large datasets, training andvalidating a single configuration often takes hours, days, or even weeks, whichlimits the achievable performance. To accelerate hyperparameter optimization,we propose a generative model for the validation error as a function oftraining set size, which is learned during the optimization process and allowsexploration of preliminary configurations on small subsets, by extrapolating tothe full dataset. We construct a Bayesian optimization procedure, dubbedFabolas, which models loss and training time as a function of dataset size andautomatically trades off high information gain about the global optimum againstcomputational cost. Experiments optimizing support vector machines and deepneural networks show that Fabolas often finds high-quality solutions 10 to 100times faster than other state-of-the-art Bayesian optimization methods or therecently proposed bandit strategy Hyperband.

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets