Hyperparameter Optimization with Factorized Multilayer Perceptrons

Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme

Abstract: In machine learning, hyperparameter optimization is a challenging task that is usually approached by experienced practitioners or in a computationally expensive brute-force manner such as grid-search. Therefore, recent research proposes to use observed hyperparameter performance on already solved problems (i.e. data sets) in order to speed up the search for promising hyperparameter configurations in the sequential model based optimization framework.