22 citations · 25 across the 4 of their papers we have counts for
4 papers
A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques
Xuetong Li, Yuan Gao, Hong Chang +11
This paper presents a selective review of statistical computation methods for massive data analysis. A huge amount of statistical methods for massive data computation have been rap…
Mini-batch Gradient Descent with Buffer
Haobo Qi, Du Huang, Yingqiu Zhu +2
In this paper, we studied a buffered mini-batch gradient descent (BMGD) algorithm for training complex model on massive datasets. The algorithm studied here is designed for fast tr…
Statistical Analysis of Fixed Mini-Batch Gradient Descent Estimator
Haobo Qi, Feifei Wang, Hansheng Wang
We study here a fixed mini-batch gradient decent (FMGD) algorithm to solve optimization problems with massive datasets. In FMGD, the whole sample is split into multiple non-overlap…
An Asymptotic Analysis of Minibatch-Based Momentum Methods for Linear Regression Models
Yuan Gao, Xuening Zhu, Haobo Qi +3
Momentum methods have been shown to accelerate the convergence of the standard gradient descent algorithm in practice and theory. In particular, the minibatch-based gradient descen…