Publications by Mireille El Gheche

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While phi-divergences have been extensively studied in convex analysis, their use in optimization problems often remains challenging. In this regard, one of the main shortcomings of existing methods is that the minimization of phi-divergences is usually pe ...

For a geoscientist, the Relative Geologic Time (RGT) is an important tool to perform chronostratigraphic analysis. However, automatically estimate an RGT image from a seismic image can be a challenging task where we have to respect seismic features, the de ...

Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the graph signals, namely the data that live ...

In this paper, we propose a scalable algorithm for spectral embedding. The latter is a standard tool for graph clustering. However, its computational bottleneck is the eigendecomposition of the graph Laplacian matrix, which prevents its application to larg ...

Graph comparison deals with identifying similarities and dissimilarities between graphs. A major obstacle is the unknown alignment of graphs, as well as the lack of accurate and inexpensive comparison metrics. In this work we introduce the filter graph dis ...

Network data appears in very diverse applications, like biological, social, or sensor networks. Clustering of network nodes into categories or communities has thus become a very common task in machine learning and data mining. Network data comes with some ...

In this article we propose a new super-resolution algorithm tailored for light field cameras, which suffer by design from a limited spatial resolution. To do so, we cast the light field super-resolution problem into an optimization problem, where the parti ...

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenes are characterized by a prevalence of human made structures, which in most of the cases, are either ...

In many applications, a dataset can be considered as a set of observed signals that live on an unknown underlying graph structure. Some of these signals may be seen as white noise that has been filtered on the graph topology by a graph filter. Hence, the k ...

Proximal splitting methods are standard tools for nonsmooth optimization. While primal-dual methods have become very popular in the last decade for their flexibility, primal methods may still be preferred for two reasons: acceleration schemes are more effe ...

We are interested in multilayer graph clustering, which aims at dividing the graph nodes into categories or communities. To do so, we propose to learn a clustering-friendly embedding of the graph nodes by solving an optimization problem that involves a fid ...

Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all avai ...

Optimal transport aims to estimate a transportation plan that minimizes a displacement cost. This is realized by optimizing the scalar product between the sought plan and the given cost, over the space of doubly stochastic matrices. When the entropy regula ...

Mireille El Gheche

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Proximity Operators of Discrete Information Divergences

Robust and adaptive approaches for Relative Geologic Time Estimation

Distributed Graph Learning With Smooth Data Priors

Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint

fGOT: Graph Distances Based on Filters and Optimal Transport

OrthoNet: Multilayer Network Data Clustering

A Nonsmooth Graph-Based Approach to Light Field Super-Resolution

Joint Graph-based Depth Refinement and Normal Estimation

Figlearn: Filter And Graph Learning Using Optimal Transport

Yapa: Accelerated Proximal Algorithm For Convex Composite Problems

Multilayer Graph Clustering With Optimized Node Embedding

Graph Heat Mixture Model Learning

Forward-Backward Splitting for Optimal Transport Based Problems

Une approche basée incertitude pour transporter un signal d’un graphe à un autre