Introduces a TAP-motivated framework and constructs explicit parameter-free spectral algorithms that achieve strong detection and weak recovery thresholds in three canonical correlated two-view models with matching lower bounds.
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math.ST 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
In the proportional high-dimensional regime, sample canonical directions in finite-rank spiked Gaussian CCA retain a deterministic fraction of population directional information with explicit CLT fluctuations around that limit.
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Optimal Spectral Algorithms for Correlated Two-view Models in High Dimensions
Introduces a TAP-motivated framework and constructs explicit parameter-free spectral algorithms that achieve strong detection and weak recovery thresholds in three canonical correlated two-view models with matching lower bounds.
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The Asymptotic Distribution of Sample Canonical Directions in Gaussian Spiked High-dimensional CCA
In the proportional high-dimensional regime, sample canonical directions in finite-rank spiked Gaussian CCA retain a deterministic fraction of population directional information with explicit CLT fluctuations around that limit.