10 citations · 38 across the 27 of their papers we have counts for
20 papers · 1 filter
Beyond Point Predictions: Distribution-Informed Prediction-Powered Inference
Maoyu Zhang, Jingfei Zhang, Xuming He
Prediction-powered inference (PPI) typically relies on point predictions on unlabeled data. When predictive distributions are available as in a wide range of applications, includin…
Estimation of High Dimensional Bounded Discrete Graphical Models via Regularized Generalized Score Matching
Xuran Meng, Jingfei Zhang, Yi Li
Graphical models for multivariate count data are widely used to characterize conditional dependence structures. For count variables with unbounded support, however, ensuring a fini…
Estimating Treatment and Spillover Effects with the Ego-Cluster Experimental Design
Xiao Liu, Feifang Hu, Jingfei Zhang
Network interference occurs when a unit's outcome depends not only on its own treatment but also on the treatments received by connected units in the network. Experimental designs…
A spectral inference method for determining the number of communities in networks
Yujia Wu, Xiucai Ding, Jingfei Zhang +2
To characterize the community structure in network data, researchers have developed various block-type models, including the stochastic block model, the degree-corrected stochastic…
Modeling Non-Uniform Hypergraphs Using Determinantal Point Processes
Yichao Chen, Jingfei Zhang, Ji Zhu
Most statistical models for networks focus on pairwise interactions between nodes. However, many real-world networks involve higher-order interactions among multiple nodes, such as…
Generalized Tensor Completion with Non-Random Missingness
Maoyu Zhang, Biao Cai, Will Wei Sun +1
Tensor completion plays a crucial role in applications such as recommender systems and medical imaging, where data are often highly incomplete. While extensive prior work has addre…
Statistical Inference on High Dimensional Gaussian Graphical Regression Models
Xuran Meng, Jingfei Zhang, Yi Li
Gaussian graphical regressions have emerged as a powerful approach for regressing the precision matrix of a Gaussian graphical model on covariates, which, unlike traditional Gaussi…
An Eigengap Ratio Test for Determining the Number of Communities in Network Data
Yujia Wu, Jingfei Zhang, Wei Lan +1
To characterize the community structure in network data, researchers have introduced various block-type models, including the stochastic block model, degree-corrected stochastic bl…
A Preferential Latent Space Model for Text Networks
Maoyu Zhang, Biao Cai, Dong Li +2
Network data enriched with textual information, referred to as text networks, arise in a wide range of applications, including email communications, scientific collaborations, and…
High-dimensional covariance regression with application to co-expression QTL detection
Rakheon Kim, Jingfei Zhang
While covariance matrices have been widely studied in many scientific fields, relatively limited progress has been made on estimating conditional covariances that permits a large c…
Learning Brain Connectivity in Social Cognition with Dynamic Network Regression
Maoyu Zhang, Biao Cai, Wenlin Dai +3
Dynamic networks have been increasingly used to characterize brain connectivity that varies during resting and task states. In such characterizations, a connectivity network is typ…
Bias-correction and Test for Mark-point Dependence with Replicated Marked Point Processes
Ganggang Xu, Jingfei Zhang, Yehua Li +1
Mark-point dependence plays a critical role in research problems that can be fitted into the general framework of marked point processes. In this work, we focus on adjusting for ma…
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…
Jointly Modeling and Clustering Tensors in High Dimensions
Biao Cai, Jingfei Zhang, Will Wei Sun
We consider the problem of jointly modeling and clustering populations of tensors by introducing a high-dimensional tensor mixture model with heterogeneous covariances. To effectiv…
High Dimensional Gaussian Graphical Regression Models with Covariates
Jingfei Zhang, Yi Li
Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We prop…
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…
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…
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…
Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis
Jingfei Zhang, Will Wei Sun, Lexin Li
Time-varying networks are fast emerging in a wide range of scientific and business disciplines. Most existing dynamic network models are limited to a single-subject and discrete-ti…
Using Maximum Entry-Wise Deviation to Test the Goodness-of-Fit for Stochastic Block Models
Jianwei Hu, Jingfei Zhang, Hong Qin +2
The stochastic block model is widely used for detecting community structures in network data. How to test the goodness-of-fit of the model is one of the fundamental problems and ha…