BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Adaptive Online Scalable Learning with Graph Feedback X-WR-TIMEZONE:America/Los_Angeles BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:2022-11-07-yanning-shen@cml.ics.uci.edu DTSTAMP:20221107T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20221107T130000 DTEND;TZID=America/Los_Angeles:20221107T140000 SUMMARY:[CML Seminar] Yanning Shen: Adaptive Online Scalable Learning with Graph Feedback LOCATION:Donald Bren Hall 4011 DESCRIPTION:Yanning Shen\, Assistant Professor of Electrical Engineering an d Computer Science\, University of California\, Irvine\n\nTitle: Adaptive Online Scalable Learning with Graph Feedback\n\nAbstract: We live in an er a of data deluge\, where pervasive media collect massive amounts of data\, often in a streaming fashion. The sheer volume makes batch analytics impo ssible\, and data are noisy\, incomplete\, and prone to outliers\, so anal ytics must often be performed in real-time. This talk introduces an online scalable function approximation scheme that adaptively learns and tracks the sought nonlinear function on the fly with quantifiable performance gua rantees\, even in adversarial environments. Building on this framework\, a scalable online learning approach with graph feedback is outlined for onl ine learning with possibly related models\, showcased on several real-worl d datasets.\n\nhttps://cml.ics.uci.edu/seminars/2022-11-07-yanning-shen X-ALT-DESC;FMTTYPE=text/html:Yanning Shen\, Assistant Pr ofessor of Electrical Engineering and Computer Science\, University of Cal ifornia\, Irvine

Title: Adaptive Online Scalable Learning wi th Graph Feedback

Abstract: We live in an era of data deluge \, where pervasive media collect massive amounts of data\, often in a stre aming fashion. The sheer volume makes batch analytics impossible\, and dat a are noisy\, incomplete\, and prone to outliers\, so analytics must often be performed in real-time. This talk introduces an online scalable functi on approximation scheme that adaptively learns and tracks the sought nonli near function on the fly with quantifiable performance guarantees\, even i n adversarial environments. Building on this framework\, a scalable online learning approach with graph feedback is outlined for online learning wit h possibly related models\, showcased on several real-world datasets.
< br>http s://cml.ics.uci.edu/seminars/2022-11-07-yanning-shen URL:https://cml.ics.uci.edu/seminars/2022-11-07-yanning-shen END:VEVENT END:VCALENDAR