Hyperparameter Optimization with Factorized Multilayer Perceptrons

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. In this paper, we propose multilayer perceptrons as surrogate models as they are able to model highly nonlinear hyperparameter response surfaces. However, since interactions of hyperparameters, data sets and metafeatures are only implicitly learned in the subsequent layers, we improve the performance of multilayer perceptrons by means of an explicit factorization of the interaction weights and call the resulting model a factorized multilayer perceptron. Additionally, we evaluate different ways of obtaining predictive uncertainty, which is a key ingredient for a decent tradeoff between exploration and exploitation. Our experimental results on two public meta data sets demonstrate the efficiency of our approach compared to a variety of published baselines. For reproduction purposes, we make our data sets and all the program code publicly available on our supplementary webpage.