55 citations · 87 across the 11 of their papers we have counts for
11 papers
Adaptive Decentralized Federated Learning for Robust Optimization
Shuyuan Wu, Feifei Wang, Yuan Gao +2
In decentralized federated learning (DFL), the presence of abnormal clients, often caused by noisy or poisoned data, can significantly disrupt the learning process and degrade the…
Penalized Sparse Covariance Regression with High Dimensional Covariates
Yuan Gao, Zhiyuan Zhang, Zhanrui Cai +3
Covariance regression offers an effective way to model the large covariance matrix with the auxiliary similarity matrices. In this work, we propose a sparse covariance regression (…
Grid Point Approximation for Distributed Nonparametric Smoothing and Prediction
Yuan Gao, Rui Pan, Feng Li +2
Kernel smoothing is a widely used nonparametric method in modern statistical analysis. The problem of efficiently conducting kernel smoothing for a massive dataset on a distributed…
A Latent Factor Model for High-Dimensional Binary Data
Jiaxin Shi, Yuan Gao, Rui Pan +1
In this study, we develop a latent factor model for analysing high-dimensional binary data. Specifically, a standard probit model is used to describe the regression relationship be…
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…
Mixture Conditional Regression with Ultrahigh Dimensional Text Data for Estimating Extralegal Factor Effects
Jiaxin Shi, Fang Wang, Yuan Gao +2
Testing judicial impartiality is a problem of fundamental importance in empirical legal studies, for which standard regression methods have been popularly used to estimate the extr…
On the asymptotic properties of a bagging estimator with a massive dataset
Yuan Gao, Riquan Zhang, Hansheng Wang
Bagging is a useful method for large-scale statistical analysis, especially when the computing resources are very limited. We study here the asymptotic properties of bagging estima…
A review of distributed statistical inference
Yuan Gao, Weidong Liu, Hansheng Wang +3
The rapid emergence of massive datasets in various fields poses a serious challenge to traditional statistical methods. Meanwhile, it provides opportunities for researchers to deve…
Testing Sufficiency for Transfer Learning
Ziqian Lin, Yuan Gao, Feifei Wang +1
Modern statistical analysis often encounters high dimensional models but with limited sample sizes. This makes the target data based statistical estimation very difficult. Then how…
Distributed Estimation and Inference for Spatial Autoregression Model with Large Scale Networks
Yimeng Ren, Zhe Li, Xuening Zhu +2
The rapid growth of online network platforms generates large-scale network data and it poses great challenges for statistical analysis using the spatial autoregression (SAR) model.…
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…