报告题目：A Probability Approximation Framework and its Applications

报告时间：2022-12-30  15:30 - 16:30

报告人：徐礼虎 副教授  澳门大学

腾讯会议ID：980 459 070

Abstract：By embedding the classical Lindeberg principle into a Markov process and using conditional expectation, we establish a general probability approximation framework. As applications, we study the error bounds of the following three approximations: approximating online stochastic gradient descents (SGDs) by stochastic differential equations (SDEs), approximating stochastic variance reduced gradients (SVRGs) by stochastic differential delay equations (SDDEs), and the approximation of ergodic measure of stable SDEs by Euler-Maruyama scheme. More applications will be discussed.  This talk is based on the joint works with P. Chen,  J. Lu,  X. Jin, and Q. M. Shao.