Approximation to stochastic variance reduced gradient Langevin dynamics by stochastic delay differential equations
Probability
2021-12-21 v2 Optimization and Control
Abstract
We study in this paper a weak approximation to stochastic variance reduced gradient Langevin dynamics by stochastic delay differential equations in Wasserstein-1 distance, and obtain a uniform error bound. Our approach is via a refined Lindeberg principle and Malliavin calculus.
Cite
@article{arxiv.2106.04357,
title = {Approximation to stochastic variance reduced gradient Langevin dynamics by stochastic delay differential equations},
author = {Peng Chen and Jianya Lu and Lihu Xu},
journal= {arXiv preprint arXiv:2106.04357},
year = {2021}
}
Comments
We update the assumption and the convergence rate, which are both better than the original version