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.
Mode-wise principal subspace pursuit and ma- trix spiked covariance model.Journal of the Royal Statistical Society Series B: Statistical Methodology, 87(1):232–255, 2025b
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A two-step spectral embedding procedure that removes irrelevant components from a knowledge matrix then projects to recover shared and heterogeneous signals for rare-disease clinical concept and patient embeddings.
A functional tensor model with common invariant subspaces and RKHS-based estimation is introduced for dynamic multilayer networks to handle shared structures, temporal smoothness, and layer heterogeneity.
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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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Enhancing Spectral Embedding through Robust and Flexible Knowledge Transfer in Electronic Health Records
A two-step spectral embedding procedure that removes irrelevant components from a knowledge matrix then projects to recover shared and heterogeneous signals for rare-disease clinical concept and patient embeddings.
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A functional tensor model for dynamic multilayer networks with common invariant subspaces and the RKHS estimation
A functional tensor model with common invariant subspaces and RKHS-based estimation is introduced for dynamic multilayer networks to handle shared structures, temporal smoothness, and layer heterogeneity.