BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UC Irvine Donald Bren School of Information & Computer Sciences - ECPv6.3.4//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:UC Irvine Donald Bren School of Information & Computer Sciences
X-ORIGINAL-URL:https://ics.uci.edu
X-WR-CALDESC:Events for UC Irvine Donald Bren School of Information & Computer Sciences
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20260308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20261101T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260604T160000
DTEND;TZID=America/Los_Angeles:20260604T170000
DTSTAMP:20261012T060832
CREATED:20260323T190218Z
LAST-MODIFIED:20260323T190218Z
UID:28370-1780588800-1780592400@ics.uci.edu
SUMMARY:Low-rank Reinforcement Learning with Heterogeneous Human Feedback
DESCRIPTION:Abstract: Modern decision-making systems\, from online marketplaces to large language models (LLMs)\, increasingly rely on high-dimensional human feedback\, where heterogeneous user preferences and massive feature spaces pose major challenges for statistical efficiency and alignment. In this talk\, I will present low-rank reinforcement learning (RL) methods that exploit latent structures in human feedback to enable scalable and theoretically grounded learning. In the first part\, we study the dynamic assortment problem in high-dimensional e-commerce and show how a low-rank structure in user–item interactions reduces the complexity of estimating personalized utilities and enables efficient exploration–exploitation strategies with provable regret guarantees. In the second part\, we extend these ideas to reinforcement learning from human feedback (RLHF) in large-scale contextual environments\, proposing a low-rank contextual framework that accommodates diverse user preferences and complex latent spaces in LLMs while providing theoretical guarantees on sample efficiency and robustness under distribution shifts.
URL:https://ics.uci.edu/event/low-rank-reinforcement-learning-with-heterogeneous-human-feedback/
LOCATION:Donald Bren Hall\, Irvine\, CA\, 92697\, United States
ATTACH;FMTTYPE=image/jpeg:https://ics.uci.edu/wp-content/uploads/2026/03/Yufeng-Liu-resized.jpg
END:VEVENT
END:VCALENDAR