3 papers
math.NA2026
A Unified Framework for Wasserstein Convergence of ULMC Methods beyond Log-Concavity: Old and New
Wanjie Lyu, Xiaojie Wang, Bin Yang
As a fundamental task across computational statistics, scientific computing and machine learning, sampling from high-dimensional probability distributions has received increasing a…
math.ST2026
Accelerating Langevin Monte Carlo via Efficient Stochastic Runge--Kutta Methods beyond Log-Concavity
Bin Yang, Xiaojie Wang
Sampling from a high-dimensional probability distribution is a fundamental algorithmic task arising in wide-ranging applications across multiple disciplines, including scientific c…
stat.ML2025
When Langevin Monte Carlo Meets Randomization: New Sampling Algorithms with Non-asymptotic Error Bounds beyond Log-Concavity and Gradient Lipschitzness
Xiaojie Wang, Bin Yang
Efficient sampling from complex and high dimensional target distributions turns out to be a fundamental task in diverse disciplines such as scientific computing, statistics and mac…