Publications associées à Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

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Estimation Error of the Constrained Lasso

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

2016

Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

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PMU-based linear state estimation of Lausanne subtransmission network: Experimental validation

Distributed State Estimation and Cooperative Path-Following Under Communication Constraints

Averaging Stochastic Gradient Descent on Riemannian Manifolds

Residual-based a posteriori error estimation for contact problems approximated by Nitsche's method

Parameter Determination of Sensor Stochastic Models under Covariate Dependency

Blowing in the Wind

Long-Term Stable Control of Motor-Imagery BCI by a Locked-in User Through Adaptive Assistance

Robust compressive sensing of sparse signals: A review

Estimation Error of the Constrained Lasso

A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers

Distributed State Estimation and Cooperative Path-Following Under Communication Constraints

Averaging Stochastic Gradient Descent on Riemannian Manifolds

Long-Term Stable Control of Motor-Imagery BCI by a Locked-in User Through Adaptive Assistance

A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers

Parameter Determination of Sensor Stochastic Models under Covariate Dependency

Blowing in the Wind

Robust compressive sensing of sparse signals: A review

Estimation Error of the Constrained Lasso

PMU-based linear state estimation of Lausanne subtransmission network: Experimental validation

Residual-based a posteriori error estimation for contact problems approximated by Nitsche's method