Scalable Hyperparameter Optimization with Products of Gaussian Process Experts

In machine learning, hyperparameter optimization is a challenging but necessary task that is usually approached in a computationally expensive manner such as grid-search. Out of this reason, surrogate based black-box optimization techniques such as sequential model-based optimization have been proposed which allow for a faster hyperparameter optimization. Recent research proposes to also integrate hyperparameter performances on past data sets to allow for a faster and more efficient hyperparameter optimization. In this paper, we use products of Gaussian process experts as surrogate models for hyperparameter optimization. Naturally, Gaussian processes are a decent choice as they offer good prediction accuracy as well as estimations about their uncertainty. Additionally, their hyperparameters can be tuned very effectively. However, in the light of large meta data sets, learning a single Gaussian process is not feasible as it involves inversion of a large kernel matrix. This directly limits their usefulness for hyperparameter optimization if large scale hyperparameter performances on past data sets are given. By using products of Gaussian process experts the scalability issues can be circumvented, however, this usually comes with the price of having less predictive accuracy. In our experiments, we show empirically that products of experts nevertheless perform very well compared to a variety of published surrogate models. Thus, we propose a surrogate model that performs as well as the current state of the art, is scalable to large scale meta knowledge, does not include hyperparameters itself and finally is even very easy to parallelize.