{"id":36366,"date":"2021-09-07T15:53:56","date_gmt":"2021-09-07T19:53:56","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&#038;p=36366"},"modified":"2021-12-30T08:50:55","modified_gmt":"2021-12-30T13:50:55","slug":"ams-seminar-w-alberto-bietti-nyu-remsen-101-or-on-zoom","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-seminar-w-alberto-bietti-nyu-remsen-101-or-on-zoom\/","title":{"rendered":"AMS Seminar w\/ Alberto Bietti (NYU) @ Remsen 101 or on Zoom"},"content":{"rendered":"<p><strong>Title:<\/strong> On the Sample Complexity of Learning under Invariance and Geometric Stability<\/p>\n<p><strong>Abstract: <\/strong>Many supervised learning problems involve high-dimensional data such as images, text, or graphs. In order to make efficient use of data, it is often useful to leverage certain geometric priors in the problem at hand, such as invariance to translations, permutation subgroups, or stability to small deformations. We study the sample complexity of learning problems where the target function presents such invariance and stability properties, by considering spherical harmonic decompositions of such functions on the sphere. We provide non-parametric rates of convergence for kernel methods, and show improvements in sample complexity by a factor equal to the size of the group when using an invariant kernel over the group, compared to the corresponding non-invariant kernel. These improvements are valid when the sample size is large enough, with an asymptotic behavior that depends on spectral properties of the group. Finally, these gains are extended beyond invariance groups to also cover geometric stability to small deformations, modeled here as subsets (not necessarily subgroups) of permutations.<\/p>\n<p>Here is the new link and meeting ID+passcode:<br \/>\n<a href=\"https:\/\/wse.zoom.us\/j\/91467375713?pwd=VjN3ekZTRFZIWS80NnpwZUFRUzRWUT09\">https:\/\/wse.zoom.us\/j\/91467375713?pwd=VjN3ekZTRFZIWS80NnpwZUFRUzRWUT09<\/a><\/p>\n<p>Meeting ID: 914 6737 5713<br \/>\nPasscode: 272254<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Title: On the Sample Complexity of Learning under Invariance and Geometric Stability Abstract: Many supervised learning problems involve high-dimensional data such as images, text, or graphs. In order to make&hellip;<\/p>\n","protected":false},"author":64,"featured_media":0,"template":"","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[260],"class_list":["post-36366","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-seminars-and-endowed-lectures","cat_seminars-and-endowed-lectures"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AMS Seminar w\/ Alberto Bietti (NYU) @ Remsen 101 or on Zoom | Department of Applied Mathematics and Statistics<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/ams\/event\/ams-seminar-w-alberto-bietti-nyu-remsen-101-or-on-zoom\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AMS Seminar w\/ Alberto Bietti (NYU) @ Remsen 101 or on Zoom | Department of Applied Mathematics and Statistics\" \/>\n<meta property=\"og:description\" content=\"Title: On the Sample Complexity of Learning under Invariance and Geometric Stability Abstract: Many supervised learning problems involve high-dimensional data such as images, text, or graphs. In order to make&hellip;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/engineering.jhu.edu\/ams\/event\/ams-seminar-w-alberto-bietti-nyu-remsen-101-or-on-zoom\/\" \/>\n<meta property=\"og:site_name\" content=\"Department of Applied Mathematics and Statistics\" \/>\n<meta property=\"article:modified_time\" content=\"2021-12-30T13:50:55+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"AMS Seminar w\/ Alberto Bietti (NYU) @ Remsen 101 or on Zoom | Department of Applied Mathematics and Statistics","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-seminar-w-alberto-bietti-nyu-remsen-101-or-on-zoom\/","og_locale":"en_US","og_type":"article","og_title":"AMS Seminar w\/ Alberto Bietti (NYU) @ Remsen 101 or on Zoom | Department of Applied Mathematics and Statistics","og_description":"Title: On the Sample Complexity of Learning under Invariance and Geometric Stability Abstract: Many supervised learning problems involve high-dimensional data such as images, text, or graphs. 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