Nicolas Keriven

49 papers A* 6A 1Misc 5Journal 34Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Compressing image encoders via latent distillation. CoRR Caroline Mazini Rodrigues, Nicolas Keriven, Thomas Maugey
2025	J	`jnl`	Backward Oversmoothing: why is it hard to train deep Graph Neural Networks? CoRR Nicolas Keriven
2025	J	`jnl`	Taxonomy of reduction matrices for Graph Coarsening. CoRR Antonin Joly, Nicolas Keriven, Aline Roumy
2024	J	`jnl`	Convergence of Message-Passing Graph Neural Networks with Generic Aggregation on Large Random Graphs. J. Mach. Learn. Res. Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter
2024	J	`jnl`	Gradient Scarcity in Graph Learning with Bilevel Optimization. Trans. Mach. Learn. Res. Hashem Ghanem, Samuel Vaiter, Nicolas Keriven
2024	A*	`conf`	Graph Coarsening with Message-Passing Guarantees. NeurIPS Antonin Joly, Nicolas Keriven
2024	J	`jnl`	Graph Coarsening with Message-Passing Guarantees. CoRR Antonin Joly, Nicolas Keriven
2024	J	`jnl`	Node Regression on Latent Position Random Graphs via Local Averaging. CoRR Martin Gjorgjevski, Nicolas Keriven, Simon Barthelmé, Yohann de Castro
2024	—	`conf`	Seeking Universal Approximation for Continuous Limits of Graph Neural Networks on Large Random Graphs. IEEECONF Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter
2023	J	`jnl`	Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs. CoRR Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter
2023	J	`jnl`	Entropic Optimal Transport on Random Graphs. SIAM J. Math. Data Sci. Nicolas Keriven
2023	J	`jnl`	Gradient scarcity with Bilevel Optimization for Graph Learning. CoRR Hashem Ghanem, Samuel Vaiter, Nicolas Keriven
2023	J	`jnl`	Supervised Learning of Analysis-Sparsity Priors With Automatic Differentiation. IEEE Signal Process. Lett. Hashem Ghanem, Joseph Salmon, Nicolas Keriven, Samuel Vaiter
2023	J	`jnl`	The Geometry of Off-the-Grid Compressed Sensing. Found. Comput. Math. Clarice Poon, Nicolas Keriven, Gabriel Peyré
2023	A*	`conf`	What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. NeurIPS Nicolas Keriven, Samuel Vaiter
2023	J	`jnl`	What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. CoRR Nicolas Keriven, Samuel Vaiter
2022	J	`jnl`	Entropic Optimal Transport in Random Graphs. CoRR Nicolas Keriven
2022	A*	`conf`	Not too little, not too much: a theoretical analysis of graph (over)smoothing. NeurIPS Nicolas Keriven
2022	J	`jnl`	Not too little, not too much: a theoretical analysis of graph (over)smoothing. CoRR Nicolas Keriven
2022	J	`jnl`	Stability of Entropic Wasserstein Barycenters and application to random geometric graphs. CoRR Marc Theveneau, Nicolas Keriven
2021	Misc	`conf`	Fast Graph Kernel with Optical Random Features. ICASSP Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay
2021	A*	`conf`	On the Universality of Graph Neural Networks on Large Random Graphs. NeurIPS Nicolas Keriven, Alberto Bietti, Samuel Vaiter
2021	J	`jnl`	On the Universality of Graph Neural Networks on Large Random Graphs. CoRR Nicolas Keriven, Alberto Bietti, Samuel Vaiter
2021	J	`jnl`	Sketching Data Sets for Large-Scale Learning: Keeping only what you need. IEEE Signal Process. Mag. Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter
2020	A*	`conf`	Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. NeurIPS Nicolas Keriven, Alberto Bietti, Samuel Vaiter
2020	J	`jnl`	Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. CoRR Nicolas Keriven, Alberto Bietti, Samuel Vaiter
2020	J	`jnl`	Fast Graph Kernel with Optical Random Features. CoRR Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay
2020	J	`jnl`	NEWMA: A New Method for Scalable Model-Free Online Change-Point Detection. IEEE Trans. Signal Process. Nicolas Keriven, Damien Garreau, Iacopo Poli
2020	J	`jnl`	Sketching Datasets for Large-Scale Learning (long version). CoRR Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter
2020	J	`jnl`	Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model. CoRR Nicolas Keriven, Samuel Vaiter
2020	J	`jnl`	Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling. CoRR Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin
2019	A	`conf`	Support Localization and the Fisher Metric for off-the-grid Sparse Regularization. AISTATS Clarice Poon, Nicolas Keriven, Gabriel Peyré
2019	A*	`conf`	Universal Invariant and Equivariant Graph Neural Networks. NeurIPS Nicolas Keriven, Gabriel Peyré
2019	J	`jnl`	Universal Invariant and Equivariant Graph Neural Networks. CoRR Nicolas Keriven, Gabriel Peyré
2018	J	`jnl`	A Dual Certificates Analysis of Compressive Off-the-Grid Recovery. CoRR Clarice Poon, Nicolas Keriven, Gabriel Peyré
2018	Misc	`conf`	Blind Source Separation Using Mixtures of Alpha-Stable Distributions. ICASSP Nicolas Keriven, Antoine Deleforge, Antoine Liutkus
2018	J	`jnl`	Instance Optimal Decoding and the Restricted Isometry Property. CoRR Nicolas Keriven, Rémi Gribonval
2018	Misc	`conf`	Large-Scale High-Dimensional Clustering with Fast Sketching. ICASSP Antoine Chatalic, Rémi Gribonval, Nicolas Keriven
2018	J	`jnl`	NEWMA: a new method for scalable model-free online change-point detection. CoRR Nicolas Keriven, Damien Garreau, Iacopo Poli
2018	J	`jnl`	Support Localization and the Fisher Metric for off-the-grid Sparse Regularization. CoRR Clarice Poon, Nicolas Keriven, Gabriel Peyré
2017	J	`jnl`	Blind Source Separation Using Mixtures of Alpha-Stable Distributions. CoRR Nicolas Keriven, Antoine Deleforge, Antoine Liutkus
2017	Misc	`conf`	Compressive K-means. ICASSP Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval
2017	J	`jnl`	Compressive Statistical Learning with Random Feature Moments. CoRR Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin
2017	—	`—`	Sketching for Large-Scale Learning of Mixture Models. (Apprentissage de modèles de mélange à large échelle par Sketching). Nicolas Keriven
2016	J	`jnl`	Compressive K-means. CoRR Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval
2016	J	`jnl`	Non-Negative Group Sparsity with Subspace Note Modelling for Polyphonic Transcription. IEEE ACM Trans. Audio Speech Lang. Process. Ken O'Hanlon, Hidehisa Nagano, Nicolas Keriven, Mark D. Plumbley
2016	J	`jnl`	Sketching for Large-Scale Learning of Mixture Models. CoRR Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez
2016	Misc	`conf`	Sketching for large-scale learning of mixture models. ICASSP Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez
2013	—	`conf`	Structured sparsity using backwards elimination for Automatic Music Transcription. MLSP Nicolas Keriven, Ken O'Hanlon, Mark D. Plumbley