2013/03/26 by Matthew B. Blaschko, Matthew Blaschko, Blaschko, Matthew
Computer Science · Decision Sciences · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multi-Criteria Decision Making #Risk and Portfolio Optimization #Statistical Methods and Inference #cs.LG
paper · pdf · doi:10.48550/arxiv.1303.6390
openalex publication_date 2013/03/26 · arxiv created 2013/03/27 · arxiv updated 2013/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The k-support norm has been recently introduced to perform correlated sparsity regularization. Although Argyriou et al. only reported experiments using squared loss, here we apply it to several other commonly used settings resulting in novel machine learning algorithms with interesting and familiar limit cases. Source code for the algorithms described here is available.