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DTSTART;TZID=America/New_York:20250321T100000
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DTSTAMP:20250430T025015
CREATED:20241205T172658Z
LAST-MODIFIED:20250219T172339Z
UID:10000441-1742551200-1742554800@www.med.unc.edu
SUMMARY:Research in Progress | Weitong Zhang
DESCRIPTION:In-person | Mary Ellen Jones 3116 \nVirtual |  ZOOM LINK  (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–a 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: Weitong Zhang 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 
URL:/compmed/event/research-in-progress-weitong-zhang/
LOCATION:Mary Ellen Jones 3116\, 116 Manning Drive\, Chapel Hill\, NC\, 27599\, United States
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