Bounce: a Reliable Bayesian Optimization Algorithm for Combinatorial and Mixed Spaces
(2023) 37th Conference on Neural Information Processing Systems, NeurIPS 2023- Abstract
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7a0f83b9-13ec-4d48-a26c-50aaf0a6e36d
- author
- Papenmeier, Leonard LU ; Nardi, Luigi LU and Poloczek, Matthias
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- in press
- subject
- keywords
- Bayesian optimization, Gaussian process, high-dimensional, optimization
- host publication
- Advances in Neural Information Processing Systems, NeurIPS 2023
- conference name
- 37th Conference on Neural Information Processing Systems, NeurIPS 2023
- conference location
- New Orleans, United States
- conference dates
- 2023-12-10 - 2023-12-16
- language
- English
- LU publication?
- yes
- id
- 7a0f83b9-13ec-4d48-a26c-50aaf0a6e36d
- alternative location
- https://arxiv.org/pdf/2307.00618.pdf
- date added to LUP
- 2023-09-22 15:02:28
- date last changed
- 2023-10-16 17:54:56
@inproceedings{7a0f83b9-13ec-4d48-a26c-50aaf0a6e36d, abstract = {{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.}}, author = {{Papenmeier, Leonard and Nardi, Luigi and Poloczek, Matthias}}, booktitle = {{Advances in Neural Information Processing Systems, NeurIPS 2023}}, keywords = {{Bayesian optimization; Gaussian process; high-dimensional; optimization}}, language = {{eng}}, title = {{Bounce: a Reliable Bayesian Optimization Algorithm for Combinatorial and Mixed Spaces}}, url = {{https://arxiv.org/pdf/2307.00618.pdf}}, year = {{2023}}, }