Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866910375424819200 |
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| author | Papenmeier, Leonard Nardi, Luigi Poloczek, Matthias |
| author_facet | Papenmeier, Leonard Nardi, Luigi Poloczek, Matthias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_00618 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces Papenmeier, Leonard Nardi, Luigi Poloczek, Matthias Machine Learning 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. |
| title | Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2307.00618 |