Provable Posterior Sampling with Score-Based Diffusion through Tilted Transport
Offered By: Fields Institute via YouTube
Course Description
Overview
Explore a cutting-edge lecture on provable posterior sampling techniques using score-based diffusion and tilted transport, presented by Jiequn Han from the Simons Foundation. Delivered as part of the Fourth Symposium on Machine Learning and Dynamical Systems at the Fields Institute, this 29-minute talk delves into advanced concepts at the intersection of machine learning and statistical inference. Gain insights into innovative approaches for tackling complex probabilistic modeling challenges and enhancing the accuracy of posterior sampling methods.
Syllabus
Provable Posterior Sampling with Score-Based Diffusion through Tilted Transport
Taught by
Fields Institute
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent