{"id":252,"date":"2022-04-05T19:08:00","date_gmt":"2022-04-05T19:08:00","guid":{"rendered":"https:\/\/groups.cs.umass.edu\/equate-ml\/?p=252"},"modified":"2022-04-07T18:13:15","modified_gmt":"2022-04-07T18:13:15","slug":"paper-coresets-for-classification-simplified-and-strengthened","status":"publish","type":"post","link":"https:\/\/groups.cs.umass.edu\/equate-ml\/2022\/04\/05\/paper-coresets-for-classification-simplified-and-strengthened\/","title":{"rendered":"Paper:  Coresets for Classification \u2013 Simplified and Strengthened"},"content":{"rendered":"\n<p>We show how to sample a small subset of points from a larger dataset, such that if we solve logistic regression, hinge loss regression (i.e., soft margin SVM), or a number of other problems used to train linear classifiers on the sampled dataset, then we obtain a near optimal solution for the full dataset. This \u2018coreset\u2019 guarantee requires sampling the subset of points according to a carefully chosen distribution, which reflects each point\u2019s importance. We use a distribution based on the l_1 Lewis weights, which are closely related to the statistical leverage scores. This allows us to significantly improve the state-of-the-art for the problem.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link\" href=\"https:\/\/arxiv.org\/abs\/2106.04254\">Paper<\/a><\/div>\n<\/div>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We show how to sample a small subset of points from a larger dataset, such that if we solve logistic regression, hinge loss regression (i.e., soft margin SVM), or a number of other problems used to train linear classifiers on the sampled dataset, then we obtain a near optimal solution for the full dataset. This &hellip; <a href=\"https:\/\/groups.cs.umass.edu\/equate-ml\/2022\/04\/05\/paper-coresets-for-classification-simplified-and-strengthened\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Paper:  Coresets for Classification \u2013 Simplified and Strengthened&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":185,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[32,34,26],"class_list":["post-252","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-32","tag-neurips","tag-paper","group-blog","hfeed"],"_links":{"self":[{"href":"https:\/\/groups.cs.umass.edu\/equate-ml\/wp-json\/wp\/v2\/posts\/252","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/groups.cs.umass.edu\/equate-ml\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/groups.cs.umass.edu\/equate-ml\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/equate-ml\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/equate-ml\/wp-json\/wp\/v2\/comments?post=252"}],"version-history":[{"count":1,"href":"https:\/\/groups.cs.umass.edu\/equate-ml\/wp-json\/wp\/v2\/posts\/252\/revisions"}],"predecessor-version":[{"id":253,"href":"https:\/\/groups.cs.umass.edu\/equate-ml\/wp-json\/wp\/v2\/posts\/252\/revisions\/253"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/equate-ml\/wp-json\/wp\/v2\/media\/185"}],"wp:attachment":[{"href":"https:\/\/groups.cs.umass.edu\/equate-ml\/wp-json\/wp\/v2\/media?parent=252"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/equate-ml\/wp-json\/wp\/v2\/categories?post=252"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/equate-ml\/wp-json\/wp\/v2\/tags?post=252"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}