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

MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs