报告题目：A Framework for Analyzing Variance Reduced Stochastic Gradient Methods and A New One

主 讲 人：梁经纬

单 位：上海交通大学

时 间：12月16日14:00

腾 讯 ID：256-424-517

摘 要：

Over the past years, variance reduced stochastic gradient methods have become increasingly popular, not only in the machine learning community, but also other areas including inverse problems and mathematical imaging to name a few. However, despite the varieties of variance reduced stochastic gradient descent methods, their analysis varies from each other. In this talk, I will first present a unified framework, under which we manage to abstract different variance reduced stochastic gradient methods into one. Then I will introduce a new stochastic method for composed optimization problems, and illustrate its performance via several imaging problems.