{"publisher":"MLResearchPress ","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."}],"status":"public","department":[{"_id":"DEP5000"},{"_id":"DEP5023"}],"oa":"1","user_id":"83781","date_created":"2025-04-17T07:58:19Z","language":[{"iso":"eng"}],"series_title":"Proceedings of Machine Learning Research","citation":{"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).","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 .","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.","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.","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).","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.","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.","bjps":"<b>Tebbe J <i>et al.</i></b> (2024) <i>Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning</i>. MLResearchPress .","short":"J. Tebbe, C. Zimmer, A. Steland, M. Lange-Hegermann, F. Mies, Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning, MLResearchPress , 2024.","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."},"publication_identifier":{"issn":["2640-3498"]},"main_file_link":[{"url":"https://proceedings.mlr.press/v238/tebbe24a.html","open_access":"1"}],"external_id":{"isi":["WOS:001221034004001"]},"author":[{"id":"85958","first_name":"Jörn","last_name":"Tebbe","full_name":"Tebbe, Jörn"},{"last_name":"Zimmer","full_name":"Zimmer, Christoph","first_name":"Christoph"},{"first_name":"Ansgar","last_name":"Steland","full_name":"Steland, Ansgar"},{"first_name":"Markus","id":"71761","last_name":"Lange-Hegermann","full_name":"Lange-Hegermann, Markus"},{"first_name":"Fabian","last_name":"Mies","full_name":"Mies, Fabian"}],"date_updated":"2025-04-17T08:00:44Z","year":"2024","_id":"12815","publication_status":"published","title":"Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning","conference":{"name":"27th International Conference on Artificial Intelligence and Statistics (AISTATS)","start_date":"2024-05-02","location":"Valencia, SPAIN"},"page":"1333-1341","type":"conference_editor_article","publication":"International Conference on Artificial Intelligence and Statistics (AISTATS), Vol. 238"}