---
_id: '12815'
abstract:
- lang: eng
  text: Active learning of physical systems must commonly respect practical safety
    constraints, which restricts the exploration of the design space. Gaussian Processes
    (GPs) and their calibrated uncertainty estimations are widely used for this purpose.
    In many technical applications the design space is explored via continuous trajectories,
    along which the safety needs to be assessed. This is particularly challenging
    for strict safety requirements in GP methods, as it employs computationally expensive
    Monte-Carlo sampling of high quantiles. We address these challenges by providing
    provable safety bounds based on the adaptively sampled median of the supremum
    of the posterior GP. Our method significantly reduces the number of samples required
    for estimating high safety probabilities, resulting in faster evaluation without
    sacrificing accuracy and exploration speed. The effectiveness of our safe active
    learning approach is demonstrated through extensive simulations and validated
    using a real-world engine example.
author:
- first_name: Jörn
  full_name: Tebbe, Jörn
  id: '85958'
  last_name: Tebbe
- first_name: Christoph
  full_name: Zimmer, Christoph
  last_name: Zimmer
- first_name: Ansgar
  full_name: Steland, Ansgar
  last_name: Steland
- first_name: Markus
  full_name: Lange-Hegermann, Markus
  id: '71761'
  last_name: Lange-Hegermann
- first_name: Fabian
  full_name: Mies, Fabian
  last_name: Mies
citation:
  ama: Tebbe J, Zimmer C, Steland A, Lange-Hegermann M, Mies F. <i>Efficiently Computable
    Safety Bounds for Gaussian Processes in Active Learning</i>. MLResearchPress ;
    2024:1333-1341.
  apa: Tebbe, J., Zimmer, C., Steland, A., Lange-Hegermann, M., &#38; Mies, F. (2024).
    Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning.
    In <i>International Conference on Artificial Intelligence and Statistics (AISTATS),
    Vol. 238</i> (pp. 1333–1341). MLResearchPress .
  bjps: <b>Tebbe J <i>et al.</i></b> (2024) <i>Efficiently Computable Safety Bounds
    for Gaussian Processes in Active Learning</i>. MLResearchPress .
  chicago: Tebbe, Jörn, Christoph Zimmer, Ansgar Steland, Markus Lange-Hegermann,
    and Fabian Mies. <i>Efficiently Computable Safety Bounds for Gaussian Processes
    in Active Learning</i>. <i>International Conference on Artificial Intelligence
    and Statistics (AISTATS), Vol. 238</i>. Proceedings of Machine Learning Research.
    MLResearchPress , 2024.
  chicago-de: Tebbe, Jörn, Christoph Zimmer, Ansgar Steland, Markus Lange-Hegermann
    und Fabian Mies. 2024. <i>Efficiently Computable Safety Bounds for Gaussian Processes
    in Active Learning</i>. <i>International Conference on Artificial Intelligence
    and Statistics (AISTATS), Vol. 238</i>. Proceedings of Machine Learning Research.
    MLResearchPress .
  din1505-2-1: '<span style="font-variant:small-caps;">Tebbe, Jörn</span> ; <span
    style="font-variant:small-caps;">Zimmer, Christoph</span> ; <span style="font-variant:small-caps;">Steland,
    Ansgar</span> ; <span style="font-variant:small-caps;">Lange-Hegermann, Markus</span>
    ; <span style="font-variant:small-caps;">Mies, Fabian</span>: <i>Efficiently Computable
    Safety Bounds for Gaussian Processes in Active Learning</i>, <i>Proceedings of
    Machine Learning Research</i> : MLResearchPress , 2024'
  havard: J. Tebbe, C. Zimmer, A. Steland, M. Lange-Hegermann, F. Mies, Efficiently
    Computable Safety Bounds for Gaussian Processes in Active Learning, MLResearchPress
    , 2024.
  ieee: J. Tebbe, C. Zimmer, A. Steland, M. Lange-Hegermann, and F. Mies, <i>Efficiently
    Computable Safety Bounds for Gaussian Processes in Active Learning</i>. MLResearchPress
    , 2024, pp. 1333–1341.
  mla: Tebbe, Jörn, et al. “Efficiently Computable Safety Bounds for Gaussian Processes
    in Active Learning.” <i>International Conference on Artificial Intelligence and
    Statistics (AISTATS), Vol. 238</i>, MLResearchPress , 2024, pp. 1333–41.
  short: J. Tebbe, C. Zimmer, A. Steland, M. Lange-Hegermann, F. Mies, Efficiently
    Computable Safety Bounds for Gaussian Processes in Active Learning, MLResearchPress
    , 2024.
  ufg: '<b>Tebbe, Jörn u. a.</b>: Efficiently Computable Safety Bounds for Gaussian
    Processes in Active Learning, o. O. 2024 (Proceedings of Machine Learning Research).'
  van: Tebbe J, Zimmer C, Steland A, Lange-Hegermann M, Mies F. Efficiently Computable
    Safety Bounds for Gaussian Processes in Active Learning. International Conference
    on Artificial Intelligence and Statistics (AISTATS), Vol. 238. MLResearchPress
    ; 2024. (Proceedings of Machine Learning Research).
conference:
  location: Valencia, SPAIN
  name: 27th International Conference on Artificial Intelligence and Statistics (AISTATS)
  start_date: 2024-05-02
date_created: 2025-04-17T07:58:19Z
date_updated: 2025-06-25T12:47:19Z
department:
- _id: DEP5000
- _id: DEP5023
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://proceedings.mlr.press/v238/tebbe24a.html
oa: '1'
page: 1333-1341
publication: International Conference on Artificial Intelligence and Statistics (AISTATS),
  Vol. 238
publication_identifier:
  issn:
  - 2640-3498
publication_status: published
publisher: 'MLResearchPress '
series_title: Proceedings of Machine Learning Research
status: public
title: Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
type: conference_editor_article
user_id: '83781'
year: '2024'
...