Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

Noisy matrix completion aims at estimating a low-rank matrix given only partial and
corrupted entries. Despite remarkable progress in designing efficient estimation algorithms …

Approximate message passing (AMP) emerges as an effective iterative paradigm for solving
high-dimensional statistical problems. However, prior AMP theory--which focused mostly on …

Inference for heteroskedastic PCA with missing data Page 1 The Annals of Statistics 2024,
Vol. 52, No. 2, 729–756 https://doi.org/10.1214/24-AOS2366 © Institute of Mathematical …

Statistical inference for principal components of spiked covariance matrices Page 1 The Annals
of Statistics 2022, Vol. 50, No. 2, 1144–1169 https://doi.org/10.1214/21-AOS2143 © Institute of …

We propose an estimator for the singular vectors of high-dimensional low-rank matrices
corrupted by additive subgaussian noise, where the noise matrix is allowed to have …

SCORE was introduced as a spectral approach to network community detection. Since many
networks have severe degree heterogeneity, the ordinary spectral clustering (OSC) …

Spectral methods are widely used to estimate eigenvectors of a low-rank signal matrix
subject to noise. These methods use the leading eigenspace of an observed matrix to …

Asymptotically efficient estimators for stochastic blockmodels: The naive MLE, the rank-constrained
MLE, and the spectral estima Page 1 Bernoulli 28(2), 2022, 1049–1073 https://doi.org/10.3150/21-BEJ1376 …

Network data are prevalent in many contemporary big data applications in which a common
interest is to unveil important latent links between different pairs of nodes. Yet a simple …