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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Two methods achieve vanishing misclassification for community detection in directed mean-field binary graphical models when T ≫ N (near-optimal), and exact recovery when T ≫ N², without knowing edge probability p.
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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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Community detection for binary graphical models in high dimension
Two methods achieve vanishing misclassification for community detection in directed mean-field binary graphical models when T ≫ N (near-optimal), and exact recovery when T ≫ N², without knowing edge probability p.