{"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/input-warping-for-bayesian-optimization-of","title":"Input Warping for Bayesian Optimization of Non-stationary Functions","arxiv_id":"1402.0929","date":"2014-02-05","proceeding":null,"authors":["Jasper Snoek","Kevin Swersky","Richard S. Zemel","Ryan P. Adams"],"abstract":"Bayesian optimization has proven to be a highly effective methodology for the\nglobal optimization of unknown, expensive and multimodal functions. The ability\nto accurately model distributions over functions is critical to the\neffectiveness of Bayesian optimization. Although Gaussian processes provide a\nflexible prior over functions which can be queried efficiently, there are\nvarious classes of functions that remain difficult to model. One of the most\nfrequently occurring of these is the class of non-stationary functions. The\noptimization of the hyperparameters of machine learning algorithms is a problem\ndomain in which parameters are often manually transformed a priori, for example\nby optimizing in \"log-space,\" to mitigate the effects of spatially-varying\nlength scale. We develop a methodology for automatically learning a wide family\nof bijective transformations or warpings of the input space using the Beta\ncumulative distribution function. We further extend the warping framework to\nmulti-task Bayesian optimization so that multiple tasks can be warped into a\njointly stationary space. On a set of challenging benchmark optimization tasks,\nwe observe that the inclusion of warping greatly improves on the\nstate-of-the-art, producing better results faster and more reliably.","url_abs":"http://arxiv.org/abs/1402.0929v3","url_pdf":"http://arxiv.org/pdf/1402.0929v3.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":"input-warping-for-bayesian-optimization-of","repo_url":"https://github.com/HIPS/Spearmint","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"global-optimization","task_name":"global-optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1402.0929","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}