Under a generalized irrepresentability condition, atomic-norm penalized precision matrix estimators recover the true pattern once the sample covariance is sufficiently close to the population one, with improved ℓ1 bounds relative to Ravikumar et al. (2011).
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From Graphical Lasso to Atomic Norms: High-Dimensional Pattern Recovery
Under a generalized irrepresentability condition, atomic-norm penalized precision matrix estimators recover the true pattern once the sample covariance is sufficiently close to the population one, with improved ℓ1 bounds relative to Ravikumar et al. (2011).