We aim to deepen the theoretical understanding of Graph Neural Networks
...
We study the convergence of message passing graph neural networks on ran...
A common issue in graph learning under the semi-supervised setting is
re...
As interest in graph data has grown in recent years, the computation of
...
We analyze graph smoothing with mean aggregation, where each node
succes...
In graph analysis, a classic task consists in computing similarity measu...
Sparsity priors are commonly used in denoising and image reconstruction....
We study the approximation power of Graph Neural Networks (GNNs) on late...
The graphlet kernel is a classical method in graph classification. It ho...
This article considers "sketched learning," or "compressive learning," a...
We study properties of Graph Convolutional Networks (GCNs) by analyzing ...
We provide statistical learning guarantees for two unsupervised learning...
In this paper, we analyse classical variants of the Spectral Clustering ...
Graph Neural Networks (GNN) come in many flavors, but should always be e...
Sparse regularization is a central technique for both machine learning (...
We consider the problem of detecting abrupt changes in the distribution ...
In this paper, we study the preservation of information in ill-posed
non...
Many problems in machine learning and imaging can be framed as an infini...
We propose a new blind source separation algorithm based on mixtures of
...
We describe a general framework --compressive statistical learning-- for...
The Lloyd-Max algorithm is a classical approach to perform K-means
clust...
Learning parameters from voluminous data can be prohibitive in terms of
...