  Hyperparameter optimization (HPO) is a central pillar in the automation of
machine learning solutions and is mainly performed via Bayesian optimization,
where a parametric surrogate is learned to approximate the black box response
function (e.g. validation error). Unfortunately, evaluating the response
function is computationally intensive. As a remedy, earlier work emphasizes the
need for transfer learning surrogates which learn to optimize hyperparameters
for an algorithm from other tasks. In contrast to previous work, we propose to
rethink HPO as a few-shot learning problem in which we train a shared deep
surrogate model to quickly adapt (with few response evaluations) to the
response function of a new task. We propose the use of a deep kernel network
for a Gaussian process surrogate that is meta-learned in an end-to-end fashion
in order to jointly approximate the response functions of a collection of
training data sets. As a result, the novel few-shot optimization of our deep
kernel surrogate leads to new state-of-the-art results at HPO compared to
several recent methods on diverse metadata sets.
