Publications associées à Regularized Bundle Methods for Convex and Non-Convex Risks

Non-convex optimization for robust multi-view imaging

We study the multi-view imaging problem where one has to reconstruct a set of l images, representing a single scene, from a few measurements made at different viewpoints. We first express the solution of the problem as the minimizer of a non-convex objecti ...

2013

Gabor dual windows using convex optimization

Nathanaël Perraudin

Redundant Gabor frames admit an infinite number of dual frames, yet only the canonical dual Gabor system, con- structed from the minimal l2-norm dual window, is widely used. This window function however, might lack desirable properties, such as good time-f ...

2013

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

On L2-Regularization for Virtual Reference Feedback Tuning

Alireza Karimi

The Virtual Reference Feedback Tuning (VRFT) approach is a design method that allow optimal feedback control laws to be derived from input-output (I/O) data only, without need of a model of the process. A drawback of this methods is that, in its standard f ...

2013

Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets

Pascal Fua, Aurélien Lucchi, Yunpeng Li

We propose a working set based approximate subgradient descent algorithm to minimize the margin-sensitive hinge loss arising from the soft constraints in max-margin learning frameworks, such as the structured SVM. We focus on the setting of general graphic ...

2013

l0-deconvolution for compressive diffusion MRI

Jean-Philippe Thiran, Yves Wiaux, Alessandro Daducci, Anna Auria Rasclosa

Diffusion MRI is a well established imaging modality providing a powerful way to non-invasively probe the structure of the white matter. Despite the potential of the technique, the intrinsic long scan times of these sequences have hampered their use in cli ...

2013

Adaptive stochastic convex optimization over networks

Ali H. Sayed

In this work, we study the task of distributed optimization over a network of learners in which each learner possesses a convex cost function, a set of affine equality constraints, and a set of convex inequality constraints. We propose a distributed diffus ...

IEEE2013

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

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

We propose an algorithmic framework for convex minimization problems of a composite function with two terms: a self-concordant function and a possibly nonsmooth regularization term. Our method is a new proximal Newton algorithm that features a local quadra ...

2013

Direct data-driven design of sparse controllers

Alireza Karimi

This paper deals with direct data-driven design of model-reference controllers whose number of parameters is constrained. Input-output (I/O) sparse controllers are introduced and proposed as an alternative to low-order controller tuning. The optimal I/O sp ...

IEEE2013

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Volkan Cevher, Anastasios Kyrillidis

The convex

\ell_1

-regularized

\log\det

divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the

\ell_1

-regularization is nonsmooth, the

\log\det

objective is n ...

2013

Regularized Bundle Methods for Convex and Non-Convex Risks

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Non-convex optimization for robust multi-view imaging

Gabor dual windows using convex optimization

To Convexify or Not? Regression with Clustering Penalties on Graphs

On L2-Regularization for Virtual Reference Feedback Tuning

Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets

l0-deconvolution for compressive diffusion MRI

Adaptive stochastic convex optimization over networks

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

Direct data-driven design of sparse controllers

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Adaptive stochastic convex optimization over networks

Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets

Non-convex optimization for robust multi-view imaging

l0-deconvolution for compressive diffusion MRI

Gabor dual windows using convex optimization

To Convexify or Not? Regression with Clustering Penalties on Graphs

On L2-Regularization for Virtual Reference Feedback Tuning

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Direct data-driven design of sparse controllers

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