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math.OC2020
ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization
Xunpeng Huang, Runxin Xu, Hao Zhou +3
Due to its simplicity and outstanding ability to generalize, stochastic gradient descent (SGD) is still the most widely used optimization method despite its slow convergence. Meanw…
math.OC2020★ 2 cited
Adaptive Gradient Methods Can Be Provably Faster than SGD after Finite Epochs
Xunpeng Huang, Hao Zhou, Runxin Xu +2
Adaptive gradient methods have attracted much attention of machine learning communities due to the high efficiency. However their acceleration effect in practice, especially in neu…
math.OC2020★ 2 cited
SPAN: A Stochastic Projected Approximate Newton Method
Xunpeng Huang, Xianfeng Liang, Zhengyang Liu +4
Second-order optimization methods have desirable convergence properties. However, the exact Newton method requires expensive computation for the Hessian and its inverse. In this pa…