MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs
This work addresses the problem of efficient black-box optimization for practitioners dealing with multi-fidelity information sources where costs are not uniform, offering an incremental improvement to existing multi-information source optimization techniques.
This paper tackles black-box optimization with multiple information sources where fidelity and query cost vary across the search space. It models the objective function and location-dependent costs using Gaussian Processes, then uses a confidence-bound acquisition function penalized by expected query cost to select the next query. The method is evaluated on hyperparameter optimization tasks for two ML classifiers and three datasets.
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.