22 citations · 26 across the 9 of their papers we have counts for
9 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…
CluBear: A Subsampling Package for Interactive Statistical Analysis with Massive Data on A Single Machine
Ke Xu, Yingqiu Zhu, Yijing Liu +1
This article introduces CluBear, a Python-based open-source package for interactive massive data analysis. The key feature of CluBear is that it enables users to conduct convenient…
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…
Improved Naive Bayes with Mislabeled Data
Qianhan Zeng, Yingqiu Zhu, Xuening Zhu +5
Labeling mistakes are frequently encountered in real-world applications. If not treated well, the labeling mistakes can deteriorate the classification performances of a model serio…
A Wasserstein distance-based spectral clustering method for transaction data analysis
Yingqiu Zhu, Danyang Huang, Bo Zhang
With the rapid development of online payment platforms, it is now possible to record massive transaction data. Clustering on transaction data significantly contributes to analyzing…
A Sequential Addressing Subsampling Method for Massive Data Analysis under Memory Constraint
Rui Pan, Yingqiu Zhu, Baishan Guo +2
The emergence of massive data in recent years brings challenges to automatic statistical inference. This is particularly true if the data are too numerous to be read into memory as…
Subsampling Spectral Clustering for Large-Scale Social Networks
Jiayi Deng, Yi Ding, Yingqiu Zhu +3
Online social network platforms such as Twitter and Sina Weibo have been extremely popular over the past 20 years. Identifying the network community of a social platform is essenti…
Automatic, Dynamic, and Nearly Optimal Learning Rate Specification by Local Quadratic Approximation
Yingqiu Zhu, Yu Chen, Danyang Huang +2
In deep learning tasks, the learning rate determines the update step size in each iteration, which plays a critical role in gradient-based optimization. However, the determination…
Efficient Estimation for Generalized Linear Models on a Distributed System with Nonrandomly Distributed Data
Feifei Wang, Danyang Huang, Yingqiu Zhu +1
Distributed systems have been widely used in practice to accomplish data analysis tasks of huge scales. In this work, we target on the estimation problem of generalized linear mode…