BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Computational Medicine - ECPv6.11.1//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:/compmed X-WR-CALDESC:Events for Computational Medicine REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20250309T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20251102T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20250320T140000 DTEND;TZID=America/New_York:20250320T150000 DTSTAMP:20250525T082859 CREATED:20250312T112830Z LAST-MODIFIED:20250312T112924Z UID:10000450-1742479200-1742482800@www.med.unc.edu SUMMARY:Seminar with Tianlong Chen\, PhD DESCRIPTION: URL:/compmed/event/seminar-with-tianlong-chen-phd/ LOCATION:Bioinformatics Building\, Room 1131\, 130 Mason Farm Rd\, Chapel Hill\, NC\, 27514\, United States END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20250321T100000 DTEND;TZID=America/New_York:20250321T110000 DTSTAMP:20250525T082859 CREATED:20241205T172658Z LAST-MODIFIED:20250219T172339Z UID:10000441-1742551200-1742554800@www.med.unc.edu SUMMARY:Research in Progress | Weitong Zhang DESCRIPTION:In-person聽|聽Mary Ellen Jones 3116 \nVirtual聽|聽聽ZOOM LINK聽 (passcode RESEARCH) \nTITLE: On exact energy guided diffusion model and diffusion-based offline reinforcement learning \nAbstract: Guided generative models are pivotal in advancing the applications of generative modeling. In this talk\, I will explore energy guidance in diffusion and flow matching models鈥揳 generalized formulation that extends beyond conventional diffusion models. By leveraging energy guidance\, generative models are encouraged to produce samples with higher energy from the target data distribution. I will introduce energy-weighted diffusion model and flow matching model\, with efficient implementation and offering new theoretical insights. In the second half of the presentation\, I will discuss the extension of this approach to offline reinforcement learning through Q-weighted iterative policy optimization\, which shows notable performance improvements across various offline RL tasks. \n\nShort Bio:聽Weitong聽Zhang聽joined the School of Data Science and Society at the University of North Carolina at Chapel Hill as an assistant professor after completing his Ph.D. degree in computer science at the University of California\, Los Angeles. His research focuses on developing robust and efficient reinforcement learning algorithms\, emphasizing generative models and their applications in scientific discovery. \n聽 URL:/compmed/event/research-in-progress-weitong-zhang/ LOCATION:Mary Ellen Jones 3116\, 116 Manning Drive\, Chapel Hill\, NC\, 27599\, United States END:VEVENT END:VCALENDAR