{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/transductive-active-learning-with-application","title":"Transductive Active Learning with Application to Safe Bayesian Optimization","arxiv_id":null,"date":"2024-07-12","proceeding":"ICML Workshop on Aligning Reinforcement Learning Experimentalists and Theorists 2024 7","authors":["Jonas Hübotter","Bhavya Sukhija","Lenart Treven","Yarden As","Andreas Krause"],"abstract":"Safe Bayesian optimization (Safe BO) is the task of learning an optimal policy within an unknown environment, while ensuring that safety constraints are not violated. We analyze Safe BO under the lens of a generalization of active learning with concrete prediction targets where sampling is restricted to an accessible region of the domain, while prediction targets may lie outside this region. We study a family of policies that sample adaptively to minimize uncertainty about prediction targets. We are the first to show, under general regularity assumptions, that such policies converge uniformly to the smallest possible uncertainty obtainable from the accessible data. Leveraging this result, we apply our framework to Safe BO and demonstrate that our policies improve substantially upon the state-of-the-art.","url_abs":"https://jonhue.github.io/assets/pdf/icml-2024-arlet.pdf","url_pdf":"https://jonhue.github.io/assets/pdf/icml-2024-arlet.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"transductive-active-learning-with-application","repo_url":"https://github.com/jonhue/transductive-active-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"jax","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"safe-exploration","task_name":"Safe Exploration"},{"task_slug":"safe-reinforcement-learning","task_name":"Safe Reinforcement Learning"},{"task_slug":"transductive-learning","task_name":"Transductive Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}