Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization

Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

Abstract: The choice of hyperparameters and the selection of algorithms is a crucial part in machine learning. Bayesian optimization methods have been used very successfully to tune hyperparameters automatically, in many cases even being able to outperform the human expert. Recently, these techniques have been massively improved by using meta-knowledge. The idea is to use knowledge of the performance of an algorithm on given other data sets to automatically accelerate the hyperparameter optimization for a new data set.