题目：Approximation to stochastic variance reduced gradient algorithms by stochastic differential delay equations

报告人： 徐礼虎 副教授（澳门大学）

时间：2022年06月02日 10:30-12:30

报告方式：腾讯会议  ID：881-601-595 密码：13579

摘要：Stochastic variance reduced gradient (SVRG) algorithm was proposed by Johnson and Zhang in NIPS (2013) and has been extensively used in training neural networks. We shall rigorously prove that SVRG can be approximated by a family of stochastic differential delay equations (SDDEs) under some conditions which include non-convex examples. It is well known that SDDEs have the effect of strong dissipations and variance reductions. Our result gives a new interpretation for SVRG. This is joint work with Peng Chen and Jianya Lu.

邀请人：宋玉林 老师