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05/24/2023
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
We aim to deepen the theoretical understanding of Graph Neural Networks ...
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04/21/2023
Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs
We study the convergence of message passing graph neural networks on ran...
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03/24/2023
Gradient scarcity with Bilevel Optimization for Graph Learning
A common issue in graph learning under the semi-supervised setting is re...
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10/19/2022
Stability of Entropic Wasserstein Barycenters and application to random geometric graphs
As interest in graph data has grown in recent years, the computation of ...
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05/24/2022
Not too little, not too much: a theoretical analysis of graph (over)smoothing
We analyze graph smoothing with mean aggregation, where each node succes...
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01/11/2022
Entropic Optimal Transport in Random Graphs
In graph analysis, a classic task consists in computing similarity measu...
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12/15/2021
Supervised learning of analysis-sparsity priors with automatic differentiation
Sparsity priors are commonly used in denoising and image reconstruction....
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05/27/2021
On the Universality of Graph Neural Networks on Large Random Graphs
We study the approximation power of Graph Neural Networks (GNNs) on late...
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10/16/2020
Fast Graph Kernel with Optical Random Features
The graphlet kernel is a classical method in graph classification. It ho...
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08/04/2020
Sketching Datasets for Large-Scale Learning (long version)
This article considers "sketched learning," or "compressive learning," a...
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06/02/2020
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
We study properties of Graph Convolutional Networks (GCNs) by analyzing ...
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04/17/2020
Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling
We provide statistical learning guarantees for two unsupervised learning...
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02/07/2020
Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model
In this paper, we analyse classical variants of the Spectral Clustering ...
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05/13/2019
Universal Invariant and Equivariant Graph Neural Networks
Graph Neural Networks (GNN) come in many flavors, but should always be e...
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10/08/2018
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization
Sparse regularization is a central technique for both machine learning (...
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05/21/2018
NEWMA: a new method for scalable model-free online change-point detection
We consider the problem of detecting abrupt changes in the distribution ...
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02/27/2018
Instance Optimal Decoding and the Restricted Isometry Property
In this paper, we study the preservation of information in ill-posed non...
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02/23/2018
A Dual Certificates Analysis of Compressive Off-the-Grid Recovery
Many problems in machine learning and imaging can be framed as an infini...
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11/13/2017
Blind Source Separation Using Mixtures of Alpha-Stable Distributions
We propose a new blind source separation algorithm based on mixtures of ...
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06/22/2017
Compressive Statistical Learning with Random Feature Moments
We describe a general framework --compressive statistical learning-- for...
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10/27/2016
Compressive K-means
The Lloyd-Max algorithm is a classical approach to perform K-means clust...
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06/09/2016
