High-dimensional Bayesian Optimization with Group Testing

Erik Orm Hellsten, Carl Hvarfner, Leonard Papenmeier, Luigi Nardi

Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Keywords: Bayesian optimization, Gaussian process, group testing, high-dimensional

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.

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.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 7837