activity
20182023
most citedCommunity Network Auto-Regression for High-Dimensional Time Series

3 citations · 10 across the 11 of their papers we have counts for

collaborators

16 papers

cs.LG2023

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…

stat.ME2023

Subsampling and Jackknifing: A Practically Convenient Solution for Large Data Analysis with Limited Computational Resources

Shuyuan Wu, Xuening Zhu, Hansheng Wang

Modern statistical analysis often encounters datasets with large sizes. For these datasets, conventional estimation methods can hardly be used immediately because practitioners oft…

stat.ME2023★ 2 cited

Distributed Logistic Regression for Massive Data with Rare Events

Xuetong Li, Xuening Zhu, Hansheng Wang

Large-scale rare events data are commonly encountered in practice. To tackle the massive rare events data, we propose a novel distributed estimation method for logistic regression…

q-bio.QM2023

CoGANPPIS: A Coevolution-enhanced Global Attention Neural Network for Protein-Protein Interaction Site Prediction

Jiaxing Guo, Xuening Zhu, Zixin Hu +1

Protein-protein interactions are of great importance in biochemical processes. Accurate prediction of protein-protein interaction sites (PPIs) is crucial for our understanding of b…

stat.ME2023

Network Autoregression for Incomplete Matrix-Valued Time Series

Xuening Zhu, Feifei Wang, Zeng Li +1

We study the dynamics of matrix-valued time series with observed network structures by proposing a matrix network autoregression model with row and column networks of the subjects.…

stat.ME2022★ 1 cited

Matrix-valued Network Autoregression Model with Latent Group Structure

Yimeng Ren, Xuening Zhu, Ganggang Xu +1

Matrix-valued time series data are frequently observed in a broad range of areas and have attracted great attention recently. In this work, we model network effects for high dimens…