Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Bounce: a Reliable Bayesian Optimization Algorithm for Combinatorial and Mixed Spaces

Papenmeier, Leonard LU orcid ; Nardi, Luigi LU and Poloczek, Matthias (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:
author
; and
organization
publishing date
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}},
}