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Sparsistency of $\ell_1$-Regularized $M$-Estimators

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li

We consider the model selection consistency or sparsistency of a broad set of

\ell_1

-regularized

M

-estimators for linear and non-linear statistical models in a unified fashion. For this purpose, we propose the local structured smoothness condition (LSS ...

2015

Régularisation zêta

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Heat kernel methods for Lifshitz theories

Partition function on spheres: How to use zeta function regularization

Sparsistency of $\ell_1$-Regularized $M$-Estimators

A Modulated Closed Form solution for Quantitative Susceptibility Mapping - A thorough evaluation and comparison to iterative methods based on edge prior knowledge

Integrable Systems of Neumann Type

Isotropic Inverse-Problem Approach for Two-Dimensional Phase Unwrapping

Enhancing statistical performance of data-driven controller tuning via L2-regularization

An Equivalence between the Lasso and Support Vector Machines

The dilaton Wess-Zumino action in higher dimensions

On Sparsity Averaging

Partition function on spheres: How to use zeta function regularization

Enhancing statistical performance of data-driven controller tuning via L2-regularization

An Equivalence between the Lasso and Support Vector Machines

Integrable Systems of Neumann Type

On Sparsity Averaging

The dilaton Wess-Zumino action in higher dimensions

Heat kernel methods for Lifshitz theories

Isotropic Inverse-Problem Approach for Two-Dimensional Phase Unwrapping

Sparsistency of $\ell_1$-Regularized $M$-Estimators

A Modulated Closed Form solution for Quantitative Susceptibility Mapping - A thorough evaluation and comparison to iterative methods based on edge prior knowledge