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.
A smooth computational transition in tensor pca.arXiv preprint arXiv:2509.09904
2 Pith papers cite this work. Polarity classification is still indexing.
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A near-optimal recovery algorithm for noisy k-XOR achieves the information-theoretic sample scaling with optimal noise dependence and is matched by low-degree lower bounds.
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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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Near Optimal Algorithms for Noisy $k$-XOR under Low-Degree Heuristic
A near-optimal recovery algorithm for noisy k-XOR achieves the information-theoretic sample scaling with optimal noise dependence and is matched by low-degree lower bounds.