Publications associées à Compressive sensing meets game theory

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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

Compressive sensing meets game theory

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(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

Sparse Dictionaries for Continuous-Domain Inverse Problems

Learning of robust spectral graph dictionaries for distributed processing

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Estimation Error of the Constrained Lasso

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Jump-Sparse and Sparse Recovery Using Potts Functionals

Sparsity in tensor optimization for optical-interferometric imaging

Sparsity averaging for radio-interferometric imaging

A geometric framework for registration of sparse images

Sparse Dictionaries for Continuous-Domain Inverse Problems

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Estimation Error of the Constrained Lasso

Sparsity in tensor optimization for optical-interferometric imaging

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

Learning of robust spectral graph dictionaries for distributed processing

Jump-Sparse and Sparse Recovery Using Potts Functionals

Sparsity averaging for radio-interferometric imaging

A geometric framework for registration of sparse images