Establishes statistical and computational optimality thresholds for common subspace estimation and inference under varying SNR regimes, including an impossibility result for adaptive confidence intervals below strong inference SNR.
Asymmetry helps: Eigenvalue and eigenvector analyses of asymmetrically perturbed low-rank matrices
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
A two-sample test for subspace equality in networks uses the Frobenius norm of projection matrix differences, with proven asymptotic normality to Gaussian under logarithmic average degree growth.
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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Statistically and Computationally Optimal Estimation and Inference of Common Subspaces
Establishes statistical and computational optimality thresholds for common subspace estimation and inference under varying SNR regimes, including an impossibility result for adaptive confidence intervals below strong inference SNR.
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Two-Sample Hypothesis Testing for Subspace Equality in Network Data
A two-sample test for subspace equality in networks uses the Frobenius norm of projection matrix differences, with proven asymptotic normality to Gaussian under logarithmic average degree growth.
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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