{"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/high-dimensional-bayesian-optimization-via","title":"High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups","arxiv_id":"1802.07028","date":"2018-02-20","proceeding":null,"authors":["Paul Rolland","Jonathan Scarlett","Ilija Bogunovic","Volkan Cevher"],"abstract":"Bayesian optimization (BO) is a popular technique for sequential black-box\nfunction optimization, with applications including parameter tuning, robotics,\nenvironmental monitoring, and more. One of the most important challenges in BO\nis the development of algorithms that scale to high dimensions, which remains a\nkey open problem despite recent progress. In this paper, we consider the\napproach of Kandasamy et al. (2015), in which the high-dimensional function\ndecomposes as a sum of lower-dimensional functions on subsets of the underlying\nvariables. In particular, we significantly generalize this approach by lifting\nthe assumption that the subsets are disjoint, and consider additive models with\narbitrary overlap among the subsets. By representing the dependencies via a\ngraph, we deduce an efficient message passing algorithm for optimizing the\nacquisition function. In addition, we provide an algorithm for learning the\ngraph from samples based on Gibbs sampling. We empirically demonstrate the\neffectiveness of our methods on both synthetic and real-world data.","url_abs":"http://arxiv.org/abs/1802.07028v2","url_pdf":"http://arxiv.org/pdf/1802.07028v2.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":"high-dimensional-bayesian-optimization-via","repo_url":"https://github.com/eric-vader/HD-BO-Additive-Models","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"additive-models","task_name":"Additive models"},{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.07028","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}