130 citations · 173 across the 11 of their papers we have counts for
5 papers · 1 filter
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…