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).
Litvak, Alain Pajor, and Nicole Tomczak-Jaegermann
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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).