Symmetrically penalized least squares with non-separable penalties approximately matches separable penalties in high-dimensional Gaussian models, quantified by finite-sample concentration inequalities, with limited advantages when parameter distribution is known and automatic adaptation when unknown
Subdifferentials of convex symmetric functions: an application of the inequality of hardy, littlewood, and p\'olya
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Approximate separability of symmetrically penalized least squares in high dimensions: characterization and consequences
Symmetrically penalized least squares with non-separable penalties approximately matches separable penalties in high-dimensional Gaussian models, quantified by finite-sample concentration inequalities, with limited advantages when parameter distribution is known and automatic adaptation when unknown