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Spectral methods for data science: A statistical perspective
Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
Robust single-cell matching and multimodal analysis using shared and distinct features
The ability to align individual cellular information from multiple experimental sources is
fundamental for a systems-level understanding of biological processes. However, currently …
fundamental for a systems-level understanding of biological processes. However, currently …
Covariate-assisted community detection in multi-layer networks
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 …
across all layers. This article proposes a tensor-based community detection method in multi …
Computational and statistical thresholds in multi-layer stochastic block models
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 …
models, focusing on the critical network density threshold for consistent community structure …
Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA
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 …
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
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 …
aligned, and the edges on each layer represent one of the multiple relations among the …
Exact community recovery in correlated stochastic block models
We consider the problem of learning latent community structure from multiple correlated
networks. We study edge-correlated stochastic block models with two balanced …
networks. We study edge-correlated stochastic block models with two balanced …
Degree-heterogeneous Latent Class Analysis for High-dimensional Discrete Data
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 …
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
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 …
of matrices with shared invariant subspaces. In particular we consider an algorithm that first …
A theorem of the alternative for personalized federated learning
A widely recognized difficulty in federated learning arises from the statistical heterogeneity
among clients: local datasets often come from different but not entirely unrelated …
among clients: local datasets often come from different but not entirely unrelated …