Spectral methods for data science: A statistical perspective

Y Chen, Y Chi, J Fan, C Ma - Foundations and Trends® in …, 2021‏ - nowpublishers.com
Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

Robust single-cell matching and multimodal analysis using shared and distinct features

B Zhu, S Chen, Y Bai, H Chen, G Liao, N Mukherjee… - Nature …, 2023‏ - nature.com
The ability to align individual cellular information from multiple experimental sources is
fundamental for a systems-level understanding of biological processes. However, currently …

Covariate-assisted community detection in multi-layer networks

S Xu, Y Zhen, J Wang - Journal of Business & Economic Statistics, 2023‏ - Taylor & Francis
Communities in multi-layer networks consist of nodes with similar connectivity patterns
across all layers. This article proposes a tensor-based community detection method in multi …

Computational and statistical thresholds in multi-layer stochastic block models

J Lei, AR Zhang, Z Zhu - The Annals of Statistics, 2024‏ - projecteuclid.org
We study the problem of community recovery and detection in multi-layer stochastic block
models, focusing on the critical network density threshold for consistent community structure …

Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA

Y Zhou, Y Chen - The Annals of Statistics, 2025‏ - projecteuclid.org
Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA Page
1 The Annals of Statistics 2025, Vol. 53, No. 1, 91–116 https://doi.org/10.1214/24-AOS2456 …

Spectral co-clustering in multi-layer directed networks

W Su, X Guo, X Chang, Y Yang - Computational Statistics & Data Analysis, 2024‏ - Elsevier
Modern network analysis often involves multi-layer network data in which the nodes are
aligned, and the edges on each layer represent one of the multiple relations among the …

Exact community recovery in correlated stochastic block models

J Gaudio, MZ Racz, A Sridhar - Conference on Learning …, 2022‏ - proceedings.mlr.press
We consider the problem of learning latent community structure from multiple correlated
networks. We study edge-correlated stochastic block models with two balanced …

Degree-heterogeneous Latent Class Analysis for High-dimensional Discrete Data

Z Lyu, L Chen, Y Gu - Journal of the American Statistical …, 2025‏ - Taylor & Francis
The latent class model is a widely used mixture model for multivariate discrete data. Besides
the existence of qualitatively heterogeneous latent classes, real data often exhibit additional …

Limit results for distributed estimation of invariant subspaces in multiple networks inference and PCA

R Zheng, M Tang - arxiv preprint arxiv:2206.04306, 2022‏ - arxiv.org
We study the problem of distributed estimation of the leading singular vectors for a collection
of matrices with shared invariant subspaces. In particular we consider an algorithm that first …

A theorem of the alternative for personalized federated learning

S Chen, Q Zheng, Q Long, WJ Su - arxiv preprint arxiv:2103.01901, 2021‏ - arxiv.org
A widely recognized difficulty in federated learning arises from the statistical heterogeneity
among clients: local datasets often come from different but not entirely unrelated …