{"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/stein-points","title":"Stein Points","arxiv_id":"1803.10161","date":"2018-03-27","proceeding":"ICML 2018 7","authors":["Wilson Ye Chen","Lester Mackey","Jackson Gorham","François-Xavier Briol","Chris. J. Oates"],"abstract":"An important task in computational statistics and machine learning is to\napproximate a posterior distribution $p(x)$ with an empirical measure supported\non a set of representative points $\\{x_i\\}_{i=1}^n$. This paper focuses on\nmethods where the selection of points is essentially deterministic, with an\nemphasis on achieving accurate approximation when $n$ is small. To this end, we\npresent `Stein Points'. The idea is to exploit either a greedy or a conditional\ngradient method to iteratively minimise a kernel Stein discrepancy between the\nempirical measure and $p(x)$. Our empirical results demonstrate that Stein\nPoints enable accurate approximation of the posterior at modest computational\ncost. In addition, theoretical results are provided to establish convergence of\nthe method.","url_abs":"http://arxiv.org/abs/1803.10161v4","url_pdf":"http://arxiv.org/pdf/1803.10161v4.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":"stein-points","repo_url":"https://github.com/wilson-ye-chen/stein_points","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.10161","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}