Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces. While Bayesian optimization has recently made significant progress in solving such problems, an in-depth analysis reveals that the current state-of-the-art methods are not reliable. Their performances degrade substantially when the unknown optima of the function do not have a certain structure. To fill the need for a reliable algorithm for combinatorial and mixed spaces, this paper proposes Bounce that relies on a novel map of various variable types into nested embeddings of increasing dimensionality. Comprehensive experiments show that Bounce reliably achieves and often even improves upon state-of-the-art performance on a variety of high-dimensional problems.
@article{arxiv.2307.00618,
title = {Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces},
author = {Leonard Papenmeier and Luigi Nardi and Matthias Poloczek},
journal= {arXiv preprint arXiv:2307.00618},
year = {2024}
}