1.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- CentraleSupélecFR21 papers
- Institut national de recherche en sciences et technologies du numériqueFR13 papers
- Université Paris-SaclayFR11 papers
- OPIS: OPtimisation Imagerie et SantéFR8 papers
- École Centrale ParisFR6 papers
- Bouygues (France)FR4 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- Laboratoire d'Informatique Gaspard-MongeFR4 papers
- ESIEE ParisFR3 papers
- KU LeuvenBE3 papers
- Université de MontréalCA3 papers
- Centre Inria de SaclayFR2 papers
26 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…
Optimizing persistent homology based functions
Mathieu Carrière, Frédéric Chazal, Marc Glisse +2
Solving optimization tasks based on functions and losses with a topological flavor is a very active, growing field of research in data science and Topological Data Analysis, with a…
NodeSig: Binary Node Embeddings via Random Walk Diffusion
Abdulkadir Çelikkanat, Fragkiskos D. Malliaros, Apostolos N. Papadopoulos
Graph Representation Learning (GRL) has become a key paradigm in network analysis, with a plethora of interdisciplinary applications. As the scale of networks increases, most of th…
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…
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…
Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images -- Extended Version
Corbineau Marie-Caroline, Kouamé Denis, Chouzenoux Emilie +2
Joint deconvolution and segmentation of ultrasound images is a challenging problem in medical imaging. By adopting a hierarchical Bayesian model, we propose an accelerated Markov c…
TNE: A Latent Model for Representation Learning on Networks
Abdulkadir Çelikkanat, Fragkiskos D. Malliaros
Network representation learning (NRL) methods aim to map each vertex into a low dimensional space by preserving the local and global structure of a given network, and in recent yea…
Compressing the Input for CNNs with the First-Order Scattering Transform
Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko +1
We study the first-order scattering transform as a candidate for reducing the signal processed by a convolutional neural network (CNN). We show theoretical and empirical evidence t…
Scattering Networks for Hybrid Representation Learning
Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang +4
Scattering networks are a class of designed Convolutional Neural Networks (CNNs) with fixed weights. We argue they can serve as generic representations for modelling images. In par…
Rational Optimization for Nonlinear Reconstruction with Approximate Penalization
Marc Castella, Jean-Christophe Pesquet, Arthur Marmin
Recovering nonlinearly degraded signal in the presence of noise is a challenging problem. In this work, this problem is tackled by minimizing the sum of a non convex least-squares…
Nonlinear Acceleration of CNNs
Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont +1
The Regularized Nonlinear Acceleration (RNA) algorithm is an acceleration method capable of improving the rate of convergence of many optimization schemes such as gradient descend,…
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…
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…
Slice-to-volume medical image registration: a survey
Enzo Ferrante, Nikos Paragios
During the last decades, the research community of medical imaging has witnessed continuous advances in image registration methods, which pushed the limits of the state-of-the-art…
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz +2
In this paper, we propose several improvements on the block-coordinate Frank-Wolfe (BCFW) algorithm from Lacoste-Julien et al. (2013) recently used to optimize the structured suppo…
Learning to Discover Sparse Graphical Models
Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux +1
We consider structure discovery of undirected graphical models from observational data. Inferring likely structures from few examples is a complex task often requiring the formulat…
A Convex Surrogate Operator for General Non-Modular Loss Functions
Jiaqian Yu, Matthew Blaschko
Empirical risk minimization frequently employs convex surrogates to underlying discrete loss functions in order to achieve computational tractability during optimization. However,…
The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses
Jiaqian Yu, Matthew Blaschko
Learning with non-modular losses is an important problem when sets of predictions are made simultaneously. The main tools for constructing convex surrogate loss functions for set p…
Deep Learning for Semantic Part Segmentation with High-Level Guidance
S. Tsogkas, I. Kokkinos, G. Papandreou +1
In this work we address the task of segmenting an object into its parts, or semantic part segmentation. We start by adapting a state-of-the-art semantic segmentation system to this…