VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning

26 Feb 2018Fanhua ShangKaiwen ZhouHongying LiuJames ChengIvor W. TsangLijun ZhangDacheng TaoLicheng Jiao

In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD). Unlike the choices of snapshot and starting points in SVRG and its proximal variant, Prox-SVRG, the two vectors of VR-SGD are set to the average and last iterate of the previous epoch, respectively... (read more)

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