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Support recovery without incoherence: A case for nonconvex regularization

Volume: 45, Issue: 6
Published: Dec 1, 2017
Abstract
We develop a new primal-dual witness proof framework that may be used to establish variable selection consistency and \ell_{\infty}bounds for sparse regression problems, even when the loss function and regularizer are nonconvex. We use this method to prove two theorems concerning support recovery and \ell_{\infty}guarantees for a regression estimator in a general setting. Notably, our theory applies to all potential stationary points of...
Paper Details
Title
Support recovery without incoherence: A case for nonconvex regularization
Published Date
Dec 1, 2017
Volume
45
Issue
6
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