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Exploring exploration in Bayesian 0ptimization

Papenmeier, Leonard LU orcid ; Cheng, Nuojin ; Becker, Stephen and Nardi, Luigi LU (2025)
Abstract
A well-balanced exploration-exploitation trade-off is crucial for successful acquisition functions in Bayesian optimization. However, there is a lack of quantitative measures for exploration, making it difficult to analyze and compare different acquisition functions. This work introduces two novel approaches - observation traveling salesman distance and observation entropy - to quantify the exploration characteristics of acquisition functions based on their selected observations. Using these measures, we examine the explorative nature of several well-known acquisition functions across a diverse set of black-box problems, uncover links between exploration and empirical performance, and reveal new relationships among existing acquisition... (More)
A well-balanced exploration-exploitation trade-off is crucial for successful acquisition functions in Bayesian optimization. However, there is a lack of quantitative measures for exploration, making it difficult to analyze and compare different acquisition functions. This work introduces two novel approaches - observation traveling salesman distance and observation entropy - to quantify the exploration characteristics of acquisition functions based on their selected observations. Using these measures, we examine the explorative nature of several well-known acquisition functions across a diverse set of black-box problems, uncover links between exploration and empirical performance, and reveal new relationships among existing acquisition functions. Beyond enabling a deeper understanding of acquisition functions, these measures also provide a foundation for guiding their design in a more principled and systematic manner. (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
host publication
Forty-First Conference on Uncertainty in Artificial Intelligence
pages
28 pages
external identifiers
  • scopus:105014761317
language
English
LU publication?
yes
id
01d454d1-cafa-4035-bcc3-0f50c36c6db0
alternative location
https://arxiv.org/abs/2502.08208
date added to LUP
2025-05-07 15:17:44
date last changed
2025-11-17 09:19:58
@inproceedings{01d454d1-cafa-4035-bcc3-0f50c36c6db0,
  abstract     = {{A well-balanced exploration-exploitation trade-off is crucial for successful acquisition functions in Bayesian optimization. However, there is a lack of quantitative measures for exploration, making it difficult to analyze and compare different acquisition functions. This work introduces two novel approaches - observation traveling salesman distance and observation entropy - to quantify the exploration characteristics of acquisition functions based on their selected observations. Using these measures, we examine the explorative nature of several well-known acquisition functions across a diverse set of black-box problems, uncover links between exploration and empirical performance, and reveal new relationships among existing acquisition functions. Beyond enabling a deeper understanding of acquisition functions, these measures also provide a foundation for guiding their design in a more principled and systematic manner.}},
  author       = {{Papenmeier, Leonard and Cheng, Nuojin and Becker, Stephen and Nardi, Luigi}},
  booktitle    = {{Forty-First Conference on Uncertainty in Artificial Intelligence}},
  language     = {{eng}},
  title        = {{Exploring exploration in Bayesian 0ptimization}},
  url          = {{https://arxiv.org/abs/2502.08208}},
  year         = {{2025}},
}