  In this short paper we investigate whether meta-learning techniques can be
used to more effectively tune the hyperparameters of machine learning models
using successive halving (SH). We propose a novel variant of the SH algorithm
(MeSH), that uses meta-regressors to determine which candidate configurations
should be eliminated at each round. We apply MeSH to the problem of tuning the
hyperparameters of a gradient-boosted decision tree model. By training and
tuning our meta-regressors using existing tuning jobs from 95 datasets, we
demonstrate that MeSH can often find a superior solution to both SH and random
search.
