{"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/scalable-and-flexible-deep-bayesian","title":"Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure","arxiv_id":"2104.11667","date":"2021-04-23","proceeding":null,"authors":["Samuel Kim","Peter Y. Lu","Charlotte Loh","Jamie Smith","Jasper Snoek","Marin Soljačić"],"abstract":"Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-box. 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