Introduces the AR(1)-MSBM for evolving multilayer networks and provides online estimators with minimax-optimal rates and community recovery guarantees under stationarity and non-stationarity via adaptive windowing.
Tony Cai and Anru Zhang
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A new matrix zonotope perturbation method with coefficient-space approximation enables faster and less conservative data-driven reachability analysis than prior CMZ or MZ approaches.
Proves approximate Gaussianity of debiased linear forms of eigenvectors in matrix denoising and spiked PCA models under Gaussian noise, then constructs bias/variance estimators yielding minimax-optimal confidence intervals without sample splitting.
Develops a toolbox for two-to-infinity norm bounds on eigenvector deviations under multiple assumption sets and derives generic conditions for perfect clustering
citing papers explorer
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Online Learning for Autoregressive Multilayer Stochastic Block Models under Stationarity and Non-Stationarity
Introduces the AR(1)-MSBM for evolving multilayer networks and provides online estimators with minimax-optimal rates and community recovery guarantees under stationarity and non-stationarity via adaptive windowing.
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Data-Driven Reachability Analysis Using Matrix Perturbation Theory
A new matrix zonotope perturbation method with coefficient-space approximation enables faster and less conservative data-driven reachability analysis than prior CMZ or MZ approaches.
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Statistical Inference for Linear Functions of Eigenvectors with Small Eigengaps
Proves approximate Gaussianity of debiased linear forms of eigenvectors in matrix denoising and spiked PCA models under Gaussian noise, then constructs bias/variance estimators yielding minimax-optimal confidence intervals without sample splitting.
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Davis-Kahan Theorem in the two-to-infinity norm and its application to perfect clustering
Develops a toolbox for two-to-infinity norm bounds on eigenvector deviations under multiple assumption sets and derives generic conditions for perfect clustering