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Bounce : Reliable High-Dimensional Bayesian Optimization 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 In Advances in Neural Information Processing Systems 36.
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.

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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
published
subject
host publication
Advances in Neural Information Processing Systems
series title
Advances in Neural Information Processing Systems
volume
36
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
external identifiers
  • scopus:85191172653
ISSN
1049-5258
language
English
LU publication?
yes
id
b70b82d1-50df-438d-a139-f0ca64943280
date added to LUP
2024-05-07 15:05:11
date last changed
2024-05-07 15:06:36
@inproceedings{b70b82d1-50df-438d-a139-f0ca64943280,
  abstract     = {{<p>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.</p>}},
  author       = {{Papenmeier, Leonard and Nardi, Luigi and Poloczek, Matthias}},
  booktitle    = {{Advances in Neural Information Processing Systems}},
  issn         = {{1049-5258}},
  language     = {{eng}},
  series       = {{Advances in Neural Information Processing Systems}},
  title        = {{Bounce : Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces}},
  volume       = {{36}},
  year         = {{2023}},
}