High-dimensional Bayesian Optimization with Group Testing

• URL: http://arxiv.org/abs/2310.03515v1
• Date: Thu, 5 Oct 2023 12:52:27 GMT
• Title: High-dimensional Bayesian Optimization with Group Testing
• Authors: Erik Orm Hellsten, Carl Hvarfner, Leonard Papenmeier, Luigi Nardi
• Abstract summary: We propose a group testing approach to identify active variables to facilitate efficient optimization in high-dimensional domains. The proposed algorithm, Group Testing Bayesian Optimization (GTBO), first runs a testing phase where groups of variables are systematically selected and tested. In the second phase, GTBO guides optimization by placing more importance on the active dimensions.
• Score: 7.12295305987761
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian optimization is an effective method for optimizing expensive-to-evaluate black-box functions. High-dimensional problems are particularly challenging as the surrogate model of the objective suffers from the curse of dimensionality, which makes accurate modeling difficult. We propose a group testing approach to identify active variables to facilitate efficient optimization in these domains. The proposed algorithm, Group Testing Bayesian Optimization (GTBO), first runs a testing phase where groups of variables are systematically selected and tested on whether they influence the objective. To that end, we extend the well-established theory of group testing to functions of continuous ranges. In the second phase, GTBO guides optimization by placing more importance on the active dimensions. By exploiting the axis-aligned subspace assumption, GTBO is competitive against state-of-the-art methods on several synthetic and real-world high-dimensional optimization tasks. Furthermore, GTBO aids in the discovery of active parameters in applications, thereby enhancing practitioners' understanding of the problem at hand.

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