BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Trustworthy Machine Learning in Complex Environments 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:2022-10-10-furong-huang@cml.ics.uci.edu DTSTAMP:20221010T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20221010T130000 DTEND;TZID=America/Los_Angeles:20221010T140000 SUMMARY:[CML Seminar] Furong Huang: Trustworthy Machine Learning in Complex Environments LOCATION:Donald Bren Hall 4011 DESCRIPTION:Furong Huang\, Assistant Professor of Computer Science\, Univer sity of Maryland\n\nTitle: Trustworthy Machine Learning in Complex Environ ments\n\nAbstract: With the burgeoning use of machine learning models\, th ere is a need to rapidly and reliably deploy models in a variety of enviro nments. These trustworthy models must be able to: (i) adapt and generalize to previously unseen worlds although trained on data that only represent a subset of the world\, (ii) allow for non-iid data\, (iii) be resilient t o (adversarial) perturbations\, and (iv) conform to social norms and make ethical decisions. In this talk\, I will cover reinforcement learning algo rithms that achieve fast adaptation by guaranteed knowledge transfer\, pri ncipled methods that measure the vulnerability and improve the robustness of RL agents\, and ethical models that make fair decisions under distribut ion shifts.\n\nhttps://cml.ics.uci.edu/seminars/2022-10-10-furong-huang X-ALT-DESC;FMTTYPE=text/html:Furong Huang\, Assistant Pr ofessor of Computer Science\, University of Maryland

Title: Trustworthy Machine Learning in Complex Environments

Abstract: With the burgeoning use of machine learning models\, there is a need to rapidly and reliably deploy models in a variety of environments. These tr ustworthy models must be able to: (i) adapt and generalize to previously u nseen worlds although trained on data that only represent a subset of the world\, (ii) allow for non-iid data\, (iii) be resilient to (adversarial) perturbations\, and (iv) conform to social norms and make ethical decision s. In this talk\, I will cover reinforcement learning algorithms that achi eve fast adaptation by guaranteed knowledge transfer\, principled methods that measure the vulnerability and improve the robustness of RL agents\, a nd ethical models that make fair decisions under distribution shifts.
< br>http s://cml.ics.uci.edu/seminars/2022-10-10-furong-huang URL:https://cml.ics.uci.edu/seminars/2022-10-10-furong-huang END:VEVENT END:VCALENDAR