{"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/probabilistic-formulations-of-regression-with","title":"Probabilistic Formulations of Regression with Mixed Guidance","arxiv_id":"1804.01575","date":"2018-04-01","proceeding":null,"authors":["Aubrey Gress","Ian Davidson"],"abstract":"Regression problems assume every instance is annotated (labeled) with a real\nvalue, a form of annotation we call \\emph{strong guidance}. In order for these\nannotations to be accurate, they must be the result of a precise experiment or\nmeasurement. However, in some cases additional \\emph{weak guidance} might be\ngiven by imprecise measurements, a domain expert or even crowd sourcing.\nCurrent formulations of regression are unable to use both types of guidance. We\npropose a regression framework that can also incorporate weak guidance based on\nrelative orderings, bounds, neighboring and similarity relations. Consider\nlearning to predict ages from portrait images, these new types of guidance\nallow weaker forms of guidance such as stating a person is in their 20s or two\npeople are similar in age. These types of annotations can be easier to generate\nthan strong guidance. We introduce a probabilistic formulation for these forms\nof weak guidance and show that the resulting optimization problems are convex.\nOur experimental results show the benefits of these formulations on several\ndata sets.","url_abs":"http://arxiv.org/abs/1804.01575v1","url_pdf":"http://arxiv.org/pdf/1804.01575v1.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":"probabilistic-formulations-of-regression-with","repo_url":"https://github.com/adgress/ICDM2016","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}