The utilization of multi-layer network structures now enables the explan...
Distributed computing is critically important for modern statistical
ana...
Large-scale networks are commonly encountered in practice (e.g., Faceboo...
We study here a fixed mini-batch gradient decent (FMGD) algorithm to sol...
Labeling mistakes are frequently encountered in real-world applications....
Bagging is a useful method for large-scale statistical analysis, especia...
The rapid emergence of massive datasets in various fields poses a seriou...
Modern statistical analysis often encounters datasets with large sizes. ...
Modern statistical analysis often encounters high dimensional models but...
Large-scale rare events data are commonly encountered in practice. To ta...
The bootstrap is a widely used procedure for statistical inference becau...
In this paper, we propose a dictionary screening method for embedding
co...
We study a fully decentralized federated learning algorithm, which is a ...
Momentum methods have been shown to accelerate the convergence of the
st...
The emergence of massive data in recent years brings challenges to autom...
This article introduces subbagging (subsample aggregating) estimation
ap...
Modern statistical analyses often encounter datasets with massive sizes ...
Massive data analysis becomes increasingly prevalent, subsampling method...
In deep learning tasks, the learning rate determines the update step siz...
Distributed systems have been widely used in practice to accomplish data...
In this work we develop a distributed least squares approximation (DLSA)...
We propose a new class of spatio-temporal models with unknown and banded...