Publications by Giovanni Chierchia

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

Yapa: Accelerated Proximal Algorithm For Convex Composite Problems

Giovanni Chierchia, Mireille El Gheche

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 ...

IEEE2021

OrthoNet: Multilayer Network Data Clustering

Pascal Frossard, Giovanni Chierchia, Mireille El Gheche

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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

Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint

Pascal Frossard, Giovanni Chierchia, Mireille El Gheche

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 ...

IEEE2019

Proximity Operators of Discrete Information Divergences

Giovanni Chierchia, Mireille El Gheche

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018