output
20172022
most citedFixed Point Strategies in Data Science

103 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

8 papers

math.ST2022★ 2 cited

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…

cs.CV2022

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…

cs.LG2021★ 6 cited

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…

cs.LG2021★ 1 cited

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…

math.OC2020★ 103 cited

Fixed Point Strategies in Data Science

Patrick L. Combettes, Jean-Christophe Pesquet

The goal of this paper is to promote the use of fixed point strategies in data science by showing that they provide a simplifying and unifying framework to model, analyze, and solv…

math.OC2020★ 28 cited

SPOQ -Over- Regularization for Sparse Signal Recovery applied to Mass Spectrometry

Afef Cherni, Emilie Chouzenoux, Laurent Duval +1

Underdetermined or ill-posed inverse problems require additional information for \ldd{d} sound solutions with tractable optimization algorithms. Sparsity yields consequent heuristi…

math.OC2018★ 21 cited

A Proximal Approach for a Class of Matrix Optimization Problems

A. Benfenati, E. Chouzenoux, J. -C. Pesquet

In recent years, there has been a growing interest in mathematical models leading to the minimization, in a symmetric matrix space, of a Bregman divergence coupled with a regulariz…

math.OC2017★ 27 cited

A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression

Luis M. Briceno-Arias, Giovanni Chierchia, Emilie Chouzenoux +1

In this paper, we propose a new optimization algorithm for sparse logistic regression based on a stochastic version of the Douglas-Rachford splitting method. Our algorithm sweeps t…