Publications related to Compressive sensing meets game theory

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

Nicolas Henri Bernard Flammarion, Scott William Pesme, Mathieu Even

In this paper, we investigate the impact of stochasticity and large stepsizes on the implicit regularisation of gradient descent (GD) and stochastic gradient descent (SGD) over diagonal linear networks. We prove the convergence of GD and SGD with macroscop ...

2023

Sparse Dictionaries for Continuous-Domain Inverse Problems

Michaël Unser, Shayan Aziznejad, Thomas Jean Debarre

We study 1D continuous-domain inverse problems for multicomponent signals. The prior assumption on these signals is that each component is sparse in a different dictionary specified by a regularization operators. We introduce a hybrid regularization functi ...

2019

Learning of robust spectral graph dictionaries for distributed processing

Pascal Frossard

We consider the problem of distributed representation of signals in sensor networks, where sensors exchange quantized information with their neighbors. The signals of interest are assumed to have a sparse representation with spectral graph dictionaries. We ...

SPRINGEROPEN2018

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

We consider design of sparse controllers for a stochastic linear system with infinite horizon quadratic objective. We formulate the non-sparse optimal solution through a semidefinite program for the second order moments of the states and inputs. Given that ...

University of Minnesota2016

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

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Rémi Gribonval, Gilles Puy

We investigate a compressive sensing framework in which the sensors introduce a distortion to the measurements in the form of unknown gains. We focus on blind calibration, using measures performed on multiple unknown (but sparse) signals and formulate the ...

Institute of Electrical and Electronics Engineers2014

Jump-Sparse and Sparse Recovery Using Potts Functionals

Martin Kurt Storath

We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and pr ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Sparsity in tensor optimization for optical-interferometric imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Anna Auria Rasclosa

Image recovery in optical interferometry is an ill-posed nonlinear inverse problem arising from incomplete power spectrum and bispectrum measurements. We review our previous work, which reformulates this nonlinear problem in the framework of tensor recover ...

IEEE2014

Sparsity averaging for radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

We propose a novel regularization method for compressive imaging in the context of the CS theory with coherent and redundant dictionaries. The approach relies on the conjecture that natural images exhibit strong average sparsity over multiple coherent fram ...

2013

A geometric framework for registration of sparse images

Pascal Frossard, Alhussein Fawzi

We examine the problem of image registration when images have a sparse representation in a dictionary of geometric features. We propose a novel algorithm for aligning images by pairing their sparse components. We show numerically that this algorithm works ...

2013

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

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Sparse Dictionaries for Continuous-Domain Inverse Problems

Jump-Sparse and Sparse Recovery Using Potts Functionals

Sparsity in tensor optimization for optical-interferometric imaging

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

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

Learning of robust spectral graph dictionaries for distributed processing

A geometric framework for registration of sparse images

Estimation Error of the Constrained Lasso

Sparsity averaging for radio-interferometric imaging