{"id":7158,"date":"2025-09-26T07:02:30","date_gmt":"2025-09-26T07:02:30","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/09\/26\/2509-20404\/"},"modified":"2025-09-26T07:02:30","modified_gmt":"2025-09-26T07:02:30","slug":"2509-20404","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/09\/26\/2509-20404\/","title":{"rendered":"Sample completion, structured correlation, and Netflix problems"},"content":{"rendered":"<p>    Sample completion, structured correlation, and Netflix problems<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2509.20404v1 Announce Type: new<br \/>\nAbstract: We develop a new high-dimensional statistical learning model which can take advantage of structured correlation in data even in the presence of randomness. We completely characterize learnability in this model in terms of VCN${}_{k,k}$-dimension (essentially $k$-dependence from Shelah&#8217;s classification theory). This model suggests a theoretical explanation for the success of certain algorithms in the 2006~Netflix Prize competition.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Leonardo N. Coregliano, Maryanthe Malliaris<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2509.20404\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Sample completion, structured correlation, and Netflix problems arXiv:2509.20404v1 Announce Type: new Abstract: We develop a new high-dimensional statistical learning model which can take advantage of structured correlation in data even in the presence of randomness. We completely characterize learnability in this model in terms of VCN${}_{k,k}$-dimension (essentially $k$-dependence from Shelah&#8217;s classification theory). This model suggests [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,3894,190,112,191],"tags":[1642,3895,345],"class_list":["post-7158","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-math-lo","category-math-st","category-stat-ml","category-stat-th","tag-correlation","tag-netflix","tag-structured"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/7158"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=7158"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/7158\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=7158"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=7158"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=7158"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}