Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks. But it is still costly if each evaluation of the objective requires training and validating the algorithm being optimized, which, for large datasets, often takes hours, days, or even weeks. To accelerate hyperparameter optimization, we propose a generative model for the validation error as a function of training set size, which is learned during the optimization process and allows exploration of preliminary configurations on small subsets, by extrapolating to the full dataset. We construct a Bayesian optimization procedure, dubbed FABOLAS, which models loss and training time as a function of dataset size and automatically trades off high information gain about the global optimum against computational cost. Experiments optimizing support vector machines and deep neural networks show that FABOLAS often finds high-quality solutions 10 to 100 times faster than other state-of-the-art Bayesian optimization methods.
REF considered hyper-parameter optimization of machine learning algorithms over a large dataset D. Supposing access to subsets of D of arbitrary sizes, they show how to exploit regularity of performance across dataset sizes to significantly speed up the optimization process for support vector machines and neural networks.
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
ArXiv
ArXiv
Computer Science Mathematics