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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Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

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

A smartphone application to determine body length for body weight estimation in children: a prospective clinical trial.

Robust compressive sensing of sparse signals: A review

Evaluation of ICA-Based ICTD for PolSAR Data Analysis Using a Sliding Window Approach: Convergence Rate, Gaussian Sources, and Spatial Correlation

Estimation Error of the Constrained Lasso

Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability

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

Sparsity-Driven Statistical Inference for Inverse Problems

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Robust compressive sensing of sparse signals: A review

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

Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework

A smartphone application to determine body length for body weight estimation in children: a prospective clinical trial.

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

Estimation Error of the Constrained Lasso

Evaluation of ICA-Based ICTD for PolSAR Data Analysis Using a Sliding Window Approach: Convergence Rate, Gaussian Sources, and Spatial Correlation

Sparsity-Driven Statistical Inference for Inverse Problems

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability