Publications by Hermina Petric Maretic

fGOT: Graph Distances Based on Filters and Optimal Transport

Pascal Frossard, Giovanni Chierchia, Hermina Petric Maretic, Mireille El Gheche

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 ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

Wasserstein-Based Graph Alignment

Pascal Frossard, Hermina Petric Maretic, Mireille El Gheche

A novel method for comparing non-aligned graphs of various sizes is proposed, based on the Wasserstein distance between graph signal distributions induced by the respective graph Laplacian matrices. Specifically, a new formulation for the one-to-many graph ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Dynamics of functional network organization through graph mixture learning

Pascal Frossard, Dimitri Nestor Alice Van De Ville, Hermina Petric Maretic, Anjali Bagunu Tarun, Ilaria Ricchi

Understanding the organizational principles of human brain activity at the systems level remains a major challenge in network neuroscience. Here, we introduce a fully data-driven approach based on graph learning to extract meaningful repeating network patt ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

Representing graphs through data with learning and optimal transport

Hermina Petric Maretic

Graphs offer a simple yet meaningful representation of relationships between data. This representation is often used in machine learning algorithms in order to incorporate structural or geometric information about data. However, it can also be used in an i ...

EPFL2021

Forward-Backward Splitting for Optimal Transport Based Problems

Pascal Frossard, Effrosyni Simou, Hermina Petric Maretic, Mireille El Gheche, Guillermo Ortiz Jimenez

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 ...

IEEE2020

Graph Laplacian Mixture Model

Pascal Frossard, Hermina Petric Maretic

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all avai ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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

Pascal Frossard, Hermina Petric Maretic, Mireille El Gheche, Bastien Cyril Pasdeloup

2019

GOT: An Optimal Transport framework for Graph comparison

Pascal Frossard, Giovanni Chierchia, Hermina Petric Maretic, Mireille El Gheche

We present a novel framework based on optimal transport for the challenging problem of comparing graphs. Specifically, we exploit the probabilistic distribution of smooth graph signals defined with respect to the graph topology. This allows us to derive an ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Graph Heat Mixture Model Learning

Pascal Frossard, Hermina Petric Maretic, Mireille El Gheche

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 ...

IEEE2018

Online Graph Learning from Sequential Data

Pascal Frossard, Hermina Petric Maretic, Ali H. Sayed, Roula Nassif, Stefan Vlaski

2018

A graph learning approach for light field image compression

Pascal Frossard, Touradj Ebrahimi, Hermina Petric Maretic, Irene Viola

In recent years, light field imaging has attracted the attention of the academic and industrial communities thanks to its enhanced rendering capabilities that allow to visualise contents in a more immersive and interactive way. However, those enhanced capa ...

Spie-Int Soc Optical Engineering2018

Graph learning under sparsity priors

Pascal Frossard, Ntorina Thanou, Hermina Petric Maretic

Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. ...

Ieee2017