Introduces symmetry-aware convex shrinkage for high-dimensional covariance estimation by selecting a symmetry group via held-out negative log-likelihood and proving regret bounds plus dominance over Ledoit-Wolf under a match condition.
Tony Cai and Harrison H
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New MIP estimator for sparse PCA under spiked covariance model with statistical guarantees and custom solver scaling to 20,000 features.
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Symmetry-Aware Convex Shrinkage for High-Dimensional Covariance Estimation
Introduces symmetry-aware convex shrinkage for high-dimensional covariance estimation by selecting a symmetry group via held-out negative log-likelihood and proving regret bounds plus dominance over Ledoit-Wolf under a match condition.
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Sparse PCA: A New Scalable Estimator Based On Integer Programming
New MIP estimator for sparse PCA under spiked covariance model with statistical guarantees and custom solver scaling to 20,000 features.