  This paper addresses black-box optimization over multiple information sources
whose both fidelity and query cost change over the search space, that is they
are location dependent. The approach uses: (i) an Augmented Gaussian Process,
recently proposed in multi-information source optimization as a single model of
the objective function over search space and sources, and (ii) a Gaussian
Process to model the location-dependent cost of each source. The former is used
into a Confidence Bound based acquisition function to select the next source
and location to query, while the latter is used to penalize the value of the
acquisition depending on the expected query cost for any source-location pair.
The proposed approach is evaluated on a set of Hyperparameters Optimization
tasks, consisting of two Machine Learning classifiers and three datasets of
different sizes.
