{"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/trimmed-density-ratio-estimation","title":"Trimmed Density Ratio Estimation","arxiv_id":"1703.03216","date":"2017-03-09","proceeding":"NeurIPS 2017 12","authors":["Song Liu","Akiko Takeda","Taiji Suzuki","Kenji Fukumizu"],"abstract":"Density ratio estimation is a vital tool in both machine learning and\nstatistical community. However, due to the unbounded nature of density ratio,\nthe estimation procedure can be vulnerable to corrupted data points, which\noften pushes the estimated ratio toward infinity. In this paper, we present a\nrobust estimator which automatically identifies and trims outliers. The\nproposed estimator has a convex formulation, and the global optimum can be\nobtained via subgradient descent. We analyze the parameter estimation error of\nthis estimator under high-dimensional settings. Experiments are conducted to\nverify the effectiveness of the estimator.","url_abs":"http://arxiv.org/abs/1703.03216v3","url_pdf":"http://arxiv.org/pdf/1703.03216v3.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":"trimmed-density-ratio-estimation","repo_url":"https://github.com/anewgithubname/Trimmed-Density-Ratio-Estimation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"density-ratio-estimation","task_name":"Density Ratio Estimation"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.03216","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}