{"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/a-note-on-k-support-norm-regularized-risk","title":"A Note on k-support Norm Regularized Risk Minimization","arxiv_id":"1303.6390","date":"2013-03-26","proceeding":null,"authors":["Matthew Blaschko"],"abstract":"The k-support norm has been recently introduced to perform correlated\nsparsity regularization. Although Argyriou et al. only reported experiments\nusing squared loss, here we apply it to several other commonly used settings\nresulting in novel machine learning algorithms with interesting and familiar\nlimit cases. Source code for the algorithms described here is available.","url_abs":"http://arxiv.org/abs/1303.6390v2","url_pdf":"http://arxiv.org/pdf/1303.6390v2.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":"a-note-on-k-support-norm-regularized-risk","repo_url":"https://github.com/blaschko/ksupport","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"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}