Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee Y uanshi Liu, Cong Fang, Tong Zhang School of Intelligence Science and Technology, Peking University

Sampling from a high-dimensional distribution serves as one of the key components in statistics, machine learning, and scientific computing, and constitutes the foundation of the fields including Bayesian statistics and generative models [Liu and Liu, 2001, Brooks et al., 2011, Song et al.,