activity
20182024
most citedMulti-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

1 citations · 2 across the 3 of their papers we have counts for

collaborators

8 papers

stat.CO20221 cited

Fast Community Detection in Dynamic and Heterogeneous Networks

Maoyu Zhang, Jingfei Zhang, Wenlin Dai

Dynamic heterogeneous networks describe the temporal evolution of interactions among nodes and edges of different types. While there is a rich literature on finding communities in…

stat.ME20221 cited

Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

Jingfei Zhang, Yi Li

Gaussian graphical regression is a powerful means that regresses the precision matrix of a Gaussian graphical model on covariates, permitting the numbers of the response variables…

stat.ME2020

Fast Network Community Detection with Profile-Pseudo Likelihood Methods

Jiangzhou Wang, Jingfei Zhang, Binghui Liu +2

The stochastic block model is one of the most studied network models for community detection. It is well-known that most algorithms proposed for fitting the stochastic block model…

stat.ME2020

Latent Network Structure Learning from High Dimensional Multivariate Point Processes

Biao Cai, Jingfei Zhang, Yongtao Guan

Learning the latent network structure from large scale multivariate point process data is an important task in a wide range of scientific and business applications. For instance, w…

stat.ML2019

Sparse Tensor Additive Regression

Botao Hao, Boxiang Wang, Pengyuan Wang +3

Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing. In this paper, we propose a sparse tensor additive regression (STAR) that model…

stat.ME2018

Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies

Jingfei Zhang, Will Wei Sun, Lexin Li

Multiple-subject network data are fast emerging in recent years, where a separate connectivity matrix is measured over a common set of nodes for each individual subject, along with…