  Data-efficient learning algorithms are essential in many practical
applications for which data collection is expensive, e.g., for the optimal
deployment of wireless systems in unknown propagation scenarios. Meta-learning
can address this problem by leveraging data from a set of related learning
tasks, e.g., from similar deployment settings. In practice, one may have
available only unlabeled data sets from the related tasks, requiring a costly
labeling procedure to be carried out before use in meta-learning. For instance,
one may know the possible positions of base stations in a given area, but not
the performance indicators achievable with each deployment. To decrease the
number of labeling steps required for meta-learning, this paper introduces an
information-theoretic active task selection mechanism, and evaluates an
instantiation of the approach for Bayesian optimization of black-box models.
