Nicolas Keriven

ORCID

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 40

Top Venues

CoRR

ICASSP

NeurIPS

IEEE Signal Process. Lett.

University of Rennes 1, France (PHD)

Education History

University of Rennes 1, France

PHD

Publications

Antonin Joly, Nicolas Keriven
Graph Coarsening with Message-Passing Guarantees. CoRR (2024)
Hashem Ghanem, Samuel Vaiter, Nicolas Keriven
Gradient Scarcity in Graph Learning with Bilevel Optimization. Trans. Mach. Learn. Res. 2024 (2024)
Clarice Poon, Nicolas Keriven, Gabriel Peyré
The Geometry of Off-the-Grid Compressed Sensing. Found. Comput. Math. 23 (1) (2023)
Nicolas Keriven
Entropic Optimal Transport on Random Graphs. SIAM J. Math. Data Sci. 5 (4) (2023)
Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter
Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs. CoRR (2023)
Hashem Ghanem, Joseph Salmon, Nicolas Keriven, Samuel Vaiter
Supervised Learning of Analysis-Sparsity Priors With Automatic Differentiation. IEEE Signal Process. Lett. 30 (2023)
Nicolas Keriven, Samuel Vaiter
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. CoRR (2023)
Hashem Ghanem, Samuel Vaiter, Nicolas Keriven
Gradient scarcity with Bilevel Optimization for Graph Learning. CoRR (2023)
Nicolas Keriven, Samuel Vaiter
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. NeurIPS (2023)
Nicolas Keriven
Not too little, not too much: a theoretical analysis of graph (over)smoothing. NeurIPS (2022)
Nicolas Keriven
Not too little, not too much: a theoretical analysis of graph (over)smoothing. CoRR (2022)
Marc Theveneau, Nicolas Keriven
Stability of Entropic Wasserstein Barycenters and application to random geometric graphs. CoRR (2022)
Nicolas Keriven
Entropic Optimal Transport in Random Graphs. CoRR (2022)
Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter
Sketching Data Sets for Large-Scale Learning: Keeping only what you need. IEEE Signal Process. Mag. 38 (5) (2021)
Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay
Fast Graph Kernel with Optical Random Features. ICASSP (2021)
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
On the Universality of Graph Neural Networks on Large Random Graphs. NeurIPS (2021)
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
On the Universality of Graph Neural Networks on Large Random Graphs. CoRR (2021)
Nicolas Keriven, Damien Garreau, Iacopo Poli
NEWMA: A New Method for Scalable Model-Free Online Change-Point Detection. IEEE Trans. Signal Process. 68 (2020)
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. NeurIPS (2020)
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. CoRR (2020)
Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin
Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling. CoRR (2020)
Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter
Sketching Datasets for Large-Scale Learning (long version). CoRR (2020)
Nicolas Keriven, Samuel Vaiter
Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model. CoRR (2020)
Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay
Fast Graph Kernel with Optical Random Features. CoRR (2020)
Nicolas Keriven, Gabriel Peyré
Universal Invariant and Equivariant Graph Neural Networks. CoRR (2019)
Nicolas Keriven, Gabriel Peyré
Universal Invariant and Equivariant Graph Neural Networks. NeurIPS (2019)
Clarice Poon, Nicolas Keriven, Gabriel Peyré
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization. AISTATS (2019)
Nicolas Keriven, Rémi Gribonval
Instance Optimal Decoding and the Restricted Isometry Property. CoRR (2018)
Nicolas Keriven, Damien Garreau, Iacopo Poli
NEWMA: a new method for scalable model-free online change-point detection. CoRR (2018)
Clarice Poon, Nicolas Keriven, Gabriel Peyré
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization. CoRR (2018)
Nicolas Keriven, Antoine Deleforge, Antoine Liutkus
Blind Source Separation Using Mixtures of Alpha-Stable Distributions. ICASSP (2018)
Clarice Poon, Nicolas Keriven, Gabriel Peyré
A Dual Certificates Analysis of Compressive Off-the-Grid Recovery. CoRR (2018)
Antoine Chatalic, Rémi Gribonval, Nicolas Keriven
Large-Scale High-Dimensional Clustering with Fast Sketching. ICASSP (2018)
Nicolas Keriven, Antoine Deleforge, Antoine Liutkus
Blind Source Separation Using Mixtures of Alpha-Stable Distributions. CoRR (2017)
Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval
Compressive K-means. ICASSP (2017)
Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin
Compressive Statistical Learning with Random Feature Moments. CoRR (2017)
Ken O'Hanlon, Hidehisa Nagano, Nicolas Keriven, Mark D. Plumbley
Non-Negative Group Sparsity with Subspace Note Modelling for Polyphonic Transcription. IEEE ACM Trans. Audio Speech Lang. Process. 24 (3) (2016)
Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval
Compressive K-means. CoRR (2016)
Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez
Sketching for large-scale learning of mixture models. ICASSP (2016)
Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez
Sketching for Large-Scale Learning of Mixture Models. CoRR (2016)
Nicolas Keriven, Ken O'Hanlon, Mark D. Plumbley
Structured sparsity using backwards elimination for Automatic Music Transcription. MLSP (2013)

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Publication Activity (10 Years)

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Nicolas Keriven