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Learning Heteroscedastic Models by Convex Programming under Group Sparsity
Popular sparse estimation methods based on $\ell_1$-relaxation, such as the Lasso and the Dantzig selector, require the knowledge of the variance of the noise in order to properly tune the regularization parameter.
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