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UID:599@fds.yale.edu
DTSTART;TZID=America/New_York:20230113T150000
DTEND;TZID=America/New_York:20230113T160000
DTSTAMP:20250916T142120Z
URL:https://fds.yale.edu/events/fds-seminar-ming-yin-ucsb/
SUMMARY:FDS Seminar: Ming Yin (UCSB)
DESCRIPTION:\n\n“Instance-Adaptive and Optimal Offline Reinforcement Lear
 ning” \nSpeaker: Ming Yin (UCSB) \nAbstract: Reinforcement Learning is b
 ecoming the mainstay of sequential decision-making problems. In particular
 \, offline reinforcement learning is considered the central framework for 
 real-life applications when online interactions are not permitted. This ta
 lk will expose the main challenges for offline RL (including distribution 
 shift\, the curse of the horizon\, and the suboptimal data) and offer our 
 solutions on how to bypass them. I will discuss how to improve the sample 
 efficiency using various techniques and show how they adapt to the hardnes
 s of individual problems. I will also briefly discuss the connection betwe
 en these methodologies and their extensions to more general settings.\nRem
 ote presentation only.\nJoin from PC\, Mac\, Linux\, iOS or Android: https
 ://yale.zoom.us/j/95770019076Or Telephone：203-432-9666 (2-ZOOM if on-cam
 pus) or 646 568 7788 One Tap Mobile: +12034329666\,\,95770019076# US (Brid
 geport)\nMeeting ID: 957 7001 9076International numbers available: https:/
 /yale.zoom.us/u/adTjb3rkTu
CATEGORIES:FDS Events,Postdoctoral Applicants
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DTSTART:20221106T010000
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