Publications associées à Learning with Structured Sparsity: From Discrete to Convex and Back.

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To Convexify or Not? Regression with Clustering Penalties on Graphs

Volkan Cevher, Luca Baldassarre, Marwa El Halabi

We consider minimization problems that are compositions of convex functions of a vector

\x \in\reals^N

with submodular set functions of its support (i.e., indices of the non-zero coefficients of

\x

). Such problems are in general difficult for large

N

...

IEEE2013

Learning with Structured Sparsity: From Discrete to Convex and Back.

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A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Jump-Sparse and Sparse Recovery Using Potts Functionals

Metric Learning with Rank and Sparsity Constraints

Geometric Variational Models for Inverse Problems in Imaging

Image reconstruction of non-planar scenes from compressed multi-view measurements

Time-Optimal Path Following for Robots With Convex-Concave Constraints Using Sequential Convex Programming

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

l0-deconvolution for compressive diffusion MRI

To Convexify or Not? Regression with Clustering Penalties on Graphs

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Time-Optimal Path Following for Robots With Convex-Concave Constraints Using Sequential Convex Programming

To Convexify or Not? Regression with Clustering Penalties on Graphs

Jump-Sparse and Sparse Recovery Using Potts Functionals

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Metric Learning with Rank and Sparsity Constraints

Image reconstruction of non-planar scenes from compressed multi-view measurements

Geometric Variational Models for Inverse Problems in Imaging

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

l0-deconvolution for compressive diffusion MRI

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices