{"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/sequential-preference-based-optimization","title":"Sequential Preference-Based Optimization","arxiv_id":"1801.02788","date":"2018-01-09","proceeding":null,"authors":["Ian Dewancker","Jakob Bauer","Michael McCourt"],"abstract":"Many real-world engineering problems rely on human preferences to guide their\ndesign and optimization. We present PrefOpt, an open source package to simplify\nsequential optimization tasks that incorporate human preference feedback. Our\napproach extends an existing latent variable model for binary preferences to\nallow for observations of equivalent preference from users.","url_abs":"http://arxiv.org/abs/1801.02788v1","url_pdf":"http://arxiv.org/pdf/1801.02788v1.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":"sequential-preference-based-optimization","repo_url":"https://github.com/prefopt/prefopt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}