16 citations
- CentraleSupélecFR5 papers
- Bouygues (France)FR4 papers
- OPIS: OPtimisation Imagerie et SantéFR4 papers
- Institut national de recherche en sciences et technologies du numériqueFR2 papers
- Université Paris-SaclayFR2 papers
- École PolytechniqueFR1 paper
- Helsinki Institute of PhysicsFI1 paper
- Institut Polytechnique de ParisFR1 paper
- Istituto Nazionale di Alta Matematica Francesco SeveriIT1 paper
- Laboratoire d'Informatique de l'École PolytechniqueFR1 paper
- Université Libre de BruxellesBE1 paper
- University of BolognaIT1 paper
8 papers
On a fixed-point continuation method for a convex optimization problem
Jean-Baptiste Fest, Tommi Heikkilä, Ignace Loris +4
We consider a variation of the classical proximal-gradient algorithm for the iterative minimization of a cost function consisting of a sum of two terms, one smooth and the other pr…
Regularized Rényi divergence minimization through Bregman proximal gradient algorithms
Thomas Guilmeau, Emilie Chouzenoux, Víctor Elvira
We study the variational inference problem of minimizing a regularized Rényi divergence over an exponential family. We propose to solve this problem with a Bregman proximal gradien…
A Variational Approach for Joint Image Recovery and Feature Extraction Based on Spatially-Varying Generalised Gaussian Models
Emilie Chouzenoux, Marie-Caroline Corbineau, Jean-Christophe Pesquet +1
The joint problem of reconstruction / feature extraction is a challenging task in image processing. It consists in performing, in a joint manner, the restoration of an image and th…
Topic-aware latent models for representation learning on networks
Abdulkadir Çelikkanat, Fragkiskos D. Malliaros
Network representation learning (NRL) methods have received significant attention over the last years thanks to their success in several graph analysis problems, including node cla…
Maximizing Influence with Graph Neural Networks
George Panagopoulos, Nikolaos Tziortziotis, Michalis Vazirgiannis +1
Finding the seed set that maximizes the influence spread over a network is a well-known NP-hard problem. Though a greedy algorithm can provide near-optimal solutions, the subproble…
Sparse Signal Reconstruction for Nonlinear Models via Piecewise Rational Optimization
Arthur Marmin, Marc Castella, Jean-Christophe Pesquet +1
We propose a method to reconstruct sparse signals degraded by a nonlinear distortion and acquired at a limited sampling rate. Our method formulates the reconstruction problem as a…