130 citations · 173 across the 11 of their papers we have counts for
15 papers
On the Universality of Graph Neural Networks on Large Random Graphs
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
We study the approximation power of Graph Neural Networks (GNNs) on latent position random graphs. In the large graph limit, GNNs are known to converge to certain "continuous" mode…
Fast Graph Kernel with Optical Random Features
Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay
The graphlet kernel is a classical method in graph classification. It however suffers from a high computation cost due to the isomorphism test it includes. As a generic proxy, and…
Sketching Datasets for Large-Scale Learning (long version)
Rémi Gribonval, Antoine Chatalic, Nicolas Keriven +3
This article considers "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e.g., clustering, classification…
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
We study properties of Graph Convolutional Networks (GCNs) by analyzing their behavior on standard models of random graphs, where nodes are represented by random latent variables a…
Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling
Rémi Gribonval, Gilles Blanchard, Nicolas Keriven +1
We provide statistical learning guarantees for two unsupervised learning tasks in the context of compressive statistical learning, a general framework for resource-efficient large-…
Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model
Nicolas Keriven, Samuel Vaiter
In this paper, we analyse classical variants of the Spectral Clustering (SC) algorithm in the Dynamic Stochastic Block Model (DSBM). Existing results show that, in the relatively s…
Universal Invariant and Equivariant Graph Neural Networks
Nicolas Keriven, Gabriel Peyré
Graph Neural Networks (GNN) come in many flavors, but should always be either invariant (permutation of the nodes of the input graph does not affect the output) or equivariant (per…
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization
Clarice Poon, Nicolas Keriven, Gabriel Peyré
Sparse regularization is a central technique for both machine learning (to achieve supervised features selection or unsupervised mixture learning) and imaging sciences (to achieve…
NEWMA: a new method for scalable model-free online change-point detection
Nicolas Keriven, Damien Garreau, Iacopo Poli
We consider the problem of detecting abrupt changes in the distribution of a multi-dimensional time series, with limited computing power and memory. In this paper, we propose a new…
Instance Optimal Decoding and the Restricted Isometry Property
Nicolas Keriven, Rémi Gribonval
In this paper, we address the question of information preservation in ill-posed, non-linear inverse problems, assuming that the measured data is close to a low-dimensional model se…
The geometry of off-the-grid compressed sensing
Clarice Poon, Nicolas Keriven, Gabriel Peyré
This paper presents a sharp geometric analysis of the recovery performance of sparse regularization. More specifically, we analyze the BLASSO method which estimates a sparse measur…
Blind Source Separation Using Mixtures of Alpha-Stable Distributions
Nicolas Keriven, Antoine Deleforge, Antoine Liutkus
We propose a new blind source separation algorithm based on mixtures of alpha-stable distributions. Complex symmetric alpha-stable distributions have been recently showed to better…
Compressive Statistical Learning with Random Feature Moments
Rémi Gribonval, Gilles Blanchard, Nicolas Keriven +1
We describe a general framework -- compressive statistical learning -- for resource-efficient large-scale learning: the training collection is compressed in one pass into a low-dim…
Compressive K-means
Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin +1
The Lloyd-Max algorithm is a classical approach to perform K-means clustering. Unfortunately, its cost becomes prohibitive as the training dataset grows large. We propose a compres…
Sketching for Large-Scale Learning of Mixture Models
Nicolas Keriven, Anthony Bourrier, Rémi Gribonval +1
Learning parameters from voluminous data can be prohibitive in terms of memory and computational requirements. We propose a "compressive learning" framework where we estimate model…