  Hyperparameter optimization aims to find the optimal hyperparameter
configuration of a machine learning model, which provides the best performance
on a validation dataset. Manual search usually leads to get stuck in a local
hyperparameter configuration, and heavily depends on human intuition and
experience. A simple alternative of manual search is random/grid search on a
space of hyperparameters, which still undergoes extensive evaluations of
validation errors in order to find its best configuration. Bayesian
optimization that is a global optimization method for black-box functions is
now popular for hyperparameter optimization, since it greatly reduces the
number of validation error evaluations required, compared to random/grid
search. Bayesian optimization generally finds the best hyperparameter
configuration from random initialization without any prior knowledge. This
motivates us to let Bayesian optimization start from the configurations that
were successful on similar datasets, which are able to remarkably minimize the
number of evaluations. In this paper, we propose deep metric learning to learn
meta-features over datasets such that the similarity over them is effectively
measured by Euclidean distance between their associated meta-features. To this
end, we introduce a Siamese network composed of deep feature and meta-feature
extractors, where deep feature extractor provides a semantic representation of
each instance in a dataset and meta-feature extractor aggregates a set of deep
features to encode a single representation over a dataset. Then, our learned
meta-features are used to select a few datasets similar to the new dataset, so
that hyperparameters in similar datasets are adopted as initializations to
warm-start Bayesian hyperparameter optimization.
