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Restricted Eigenvalues Beyond Gaussian Width: Threshold Occupancy under Heavy Tails

Fu, Shi et al.
arXiv (All) · Papers
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machine-learning
Restricted eigenvalue (RE) bounds govern stable recovery by norm-regularized estimators. For isotropic sub-Gaussian measurements, the benchmark sample size is $1+w(A)^2$, where $w(A)$ is the Gaussian width of the normalized descent cone. The COLT 2015 open-problem note (Banerjee et al., 2015) asked
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