Papers › Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces

Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces

2 Jul 2023NeurIPS 2023 11arXiv:2307.00618archive 2025-07-28

Leonard Papenmeier, Luigi Nardi, Matthias Poloczek

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.

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