Nicolas Keriven

According to our database, Nicolas Keriven authored at least 31 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Gradient Scarcity in Graph Learning with Bilevel Optimization.

Hashem Ghanem, Samuel Vaiter, Nicolas Keriven

Trans. Mach. Learn. Res., 2024

Node Regression on Latent Position Random Graphs via Local Averaging.

Martin Gjorgjevski, Nicolas Keriven, Simon Barthelmé, Yohann De Castro

CoRR, 2024

Graph Coarsening with Message-Passing Guarantees.

Antonin Joly, Nicolas Keriven

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Entropic Optimal Transport on Random Graphs.

Nicolas Keriven

SIAM J. Math. Data Sci., December, 2023

The Geometry of Off-the-Grid Compressed Sensing.

Clarice Poon, Nicolas Keriven, Gabriel Peyré

Found. Comput. Math., February, 2023

Supervised Learning of Analysis-Sparsity Priors With Automatic Differentiation.

Hashem Ghanem, Joseph Salmon, Nicolas Keriven, Samuel Vaiter

IEEE Signal Process. Lett., 2023

Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs.

Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter

CoRR, 2023

Gradient scarcity with Bilevel Optimization for Graph Learning.

Hashem Ghanem, Samuel Vaiter, Nicolas Keriven

CoRR, 2023

What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding.

Nicolas Keriven, Samuel Vaiter

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Stability of Entropic Wasserstein Barycenters and application to random geometric graphs.

Marc Theveneau, Nicolas Keriven

CoRR, 2022

Not too little, not too much: a theoretical analysis of graph (over)smoothing.

Nicolas Keriven

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Sketching Data Sets for Large-Scale Learning: Keeping only what you need.

Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter

IEEE Signal Process. Mag., 2021

On the Universality of Graph Neural Networks on Large Random Graphs.

Nicolas Keriven, Alberto Bietti, Samuel Vaiter

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Fast Graph Kernel with Optical Random Features.

Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay

Proceedings of the IEEE International Conference on Acoustics, 2021

2020

NEWMA: A New Method for Scalable Model-Free Online Change-Point Detection.

Nicolas Keriven, Damien Garreau, Iacopo Poli

IEEE Trans. Signal Process., 2020

Sketching Datasets for Large-Scale Learning (long version).

Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter

CoRR, 2020

Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling.

Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin

CoRR, 2020

Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model.

Nicolas Keriven, Samuel Vaiter

CoRR, 2020

Convergence and Stability of Graph Convolutional Networks on Large Random Graphs.

Nicolas Keriven, Alberto Bietti, Samuel Vaiter

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Universal Invariant and Equivariant Graph Neural Networks.

Nicolas Keriven, Gabriel Peyré

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Support Localization and the Fisher Metric for off-the-grid Sparse Regularization.

Clarice Poon, Nicolas Keriven, Gabriel Peyré

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Instance Optimal Decoding and the Restricted Isometry Property.

Nicolas Keriven, Rémi Gribonval

CoRR, 2018

A Dual Certificates Analysis of Compressive Off-the-Grid Recovery.

Clarice Poon, Nicolas Keriven, Gabriel Peyré

CoRR, 2018

Blind Source Separation Using Mixtures of Alpha-Stable Distributions.

Nicolas Keriven, Antoine Deleforge, Antoine Liutkus

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Large-Scale High-Dimensional Clustering with Fast Sketching.

Antoine Chatalic, Rémi Gribonval, Nicolas Keriven

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017

Sketching for Large-Scale Learning of Mixture Models. (Apprentissage de modèles de mélange à large échelle par Sketching).

Nicolas Keriven

PhD thesis, 2017

Compressive Statistical Learning with Random Feature Moments.

Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin

CoRR, 2017

Compressive K-means.

Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016

Non-Negative Group Sparsity with Subspace Note Modelling for Polyphonic Transcription.

Ken O'Hanlon, Hidehisa Nagano, Nicolas Keriven, Mark D. Plumbley

IEEE ACM Trans. Audio Speech Lang. Process., 2016

Sketching for large-scale learning of mixture models.

Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez

Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

2013

Structured sparsity using backwards elimination for Automatic Music Transcription.

Nicolas Keriven, Ken O'Hanlon, Mark D. Plumbley

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2013