1 citations · 1 across the 2 of their papers we have counts for
2 papers
math.OC2022
A convergence study of SGD-type methods for stochastic optimization
Tiannan Xiao, Guoguo Yang
In this paper, we first reinvestigate the convergence of vanilla SGD method in the sense of under more general learning rates conditions and a more general convex assumption,…
math.OC2022★ 1 cited
Revisiting the central limit theorems for the SGD-type methods
Tiejun Li, Tiannan Xiao, Guoguo Yang
We revisited the central limit theorem (CLT) for stochastic gradient descent (SGD) type methods, including the vanilla SGD, momentum SGD and Nesterov accelerated SGD methods with c…