{"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/predictive-entropy-search-for-bayesian","title":"Predictive Entropy Search for Bayesian Optimization with Unknown Constraints","arxiv_id":"1502.05312","date":"2015-02-18","proceeding":null,"authors":["José Miguel Hernández-Lobato","Michael A. Gelbart","Matthew W. Hoffman","Ryan P. Adams","Zoubin Ghahramani"],"abstract":"Unknown constraints arise in many types of expensive black-box optimization\nproblems. Several methods have been proposed recently for performing Bayesian\noptimization with constraints, based on the expected improvement (EI)\nheuristic. However, EI can lead to pathologies when used with constraints. For\nexample, in the case of decoupled constraints---i.e., when one can\nindependently evaluate the objective or the constraints---EI can encounter a\npathology that prevents exploration. Additionally, computing EI requires a\ncurrent best solution, which may not exist if none of the data collected so far\nsatisfy the constraints. By contrast, information-based approaches do not\nsuffer from these failure modes. In this paper, we present a new\ninformation-based method called Predictive Entropy Search with Constraints\n(PESC). We analyze the performance of PESC and show that it compares favorably\nto EI-based approaches on synthetic and benchmark problems, as well as several\nreal-world examples. We demonstrate that PESC is an effective algorithm that\nprovides a promising direction towards a unified solution for constrained\nBayesian optimization.","url_abs":"http://arxiv.org/abs/1502.05312v2","url_pdf":"http://arxiv.org/pdf/1502.05312v2.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":"predictive-entropy-search-for-bayesian","repo_url":"https://github.com/chongkewu/PESC-HPC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1502.05312","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}