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	Pour les 31 documents Envoyer sur ORCID RSS ATOM Exporter BibTeX XML-TEI CSV RTF EndNote PDF HTML Export avancé	Page : Page précédente 1 2 Page suivante	triés par Pertinence Auteur A→Z Auteur Z→A Titre A→Z Titre Z→A Date de publication croissante Date de publication décroissante Date de dépôt croissante Date de dépôt décroissante

		On the Universality of Graph Neural Networks on Large Random Graphs Nicolas Keriven , Alberto Bietti , Samuel Vaiter NeurIPS 2021 - 35th Conference on Neural Information Processing Systems, Dec 2021, Virtual, Canada Communication dans un congrès hal-03382553v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Fast Graph Kernel with Optical Random Features Hashem Ghanem , Nicolas Keriven , Nicolas Tremblay ICASSP 2021 - IEEE International Conference on Acoustics, Speech, and Signal Processing, Jun 2021, Toronto, Canada. ⟨10.1109/ICASSP39728.2021.9413614⟩ Communication dans un congrès hal-02976716v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Gradient scarcity with Bilevel Optimization for Graph Learning Hashem Ghanem , Samuel Vaiter , Nicolas Keriven 2023 Pré-publication, Document de travail hal-04041721v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Entropic Optimal Transport in Random Graphs Nicolas Keriven SIAM Journal on Mathematics of Data Science, 2023, 5 (4), pp.1028-1050. ⟨10.1137/22M1518281⟩ Article dans une revue hal-03576738v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Sketching Data Sets for Large-Scale Learning: Keeping only what you need Rémi Gribonval , Antoine Chatalic , Nicolas Keriven , Vincent Schellekens , Laurent Jacques , et al. IEEE Signal Processing Magazine, 2021, 38 (5), pp.12-36. ⟨10.1109/MSP.2021.3092574⟩ Article dans une revue hal-03350599v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs Matthieu Cordonnier , Nicolas Keriven , Nicolas Tremblay , Samuel Vaiter 2023 Pré-publication, Document de travail hal-04059402v2	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Universal Invariant and Equivariant Graph Neural Networks Nicolas Keriven , Gabriel Peyré Advances in Neural Information Processing Systems (NeurIPS), 2019, Vancouver, Canada Communication dans un congrès hal-02484980v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Random Graphs Matthieu Cordonnier , Nicolas Keriven , Nicolas Tremblay , Samuel Vaiter GSP 2023 - 6th Graph Signal Processing workshop, Jun 2023, Oxford, United Kingdom. pp.1-3 Communication dans un congrès hal-04106511v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Sketching for Large-Scale Learning of Mixture Models Nicolas Keriven , Anthony Bourrier , Rémi Gribonval , Patrick Pérez Information and Inference, 2018, 7 (3), pp.447-508. ⟨10.1093/imaiai/iax015⟩ Article dans une revue hal-01329195v2	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Projections aléatoires pour l'apprentissage compressif Antoine Chatalic , Nicolas Keriven , Rémi Gribonval GRETSI 2019 − XXVIIème Colloque francophone de traitement du signal et des images, Aug 2019, Lille, France. pp.1-4 Communication dans un congrès hal-02154803v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model Nicolas Keriven , Samuel Vaiter Electronic Journal of Statistics , 2022, 16 (1), pp.1330 - 1366. ⟨10.1214/22-ejs1986⟩ Article dans une revue hal-02484970v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Sketching Datasets for Large-Scale Learning (long version) Rémi Gribonval , Antoine Chatalic , Nicolas Keriven , Vincent Schellekens , Laurent Jacques , et al. 2021 Pré-publication, Document de travail hal-02909766v2	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		SketchMLbox -- A MATLAB toolbox for large-scale mixture learning Nicolas Keriven 2018, ⟨swh:1:dir:a76f6aeb81ce73e178b8b822029d2c721ba24117;origin=https://hal.archives-ouvertes.fr/hal-02960718;visit=swh:1:snp:55fd14b490c1aef81d2582d91d911a71d0a159cf;anchor=swh:1:rev:d8de5cbab0e40297dd0d03d5c93895c568a30a99;path=/⟩ Logiciel hal-02960718v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Blind Source Separation Using Mixtures of Alpha-Stable Distributions Nicolas Keriven , Antoine Deleforge , Antoine Liutkus ICASSP: International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Canada. pp.771-775, ⟨10.1109/ICASSP.2018.8462095⟩ Communication dans un congrès hal-01633215v3	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Not too little, not too much: a theoretical analysis of graph (over)smoothing Nicolas Keriven NeurIPS 2022 - 36th Conference on Neural Information Processing Systems, Nov 2022, New Orleans, United States Communication dans un congrès hal-03778245v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		The geometry of off-the-grid compressed sensing Clarice Poon , Nicolas Keriven , Gabriel Peyré Foundations of Computational Mathematics, 2023, 23, pp.241-327. ⟨10.1007/s10208-021-09545-5⟩ Article dans une revue hal-02484957v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Support Localization and the Fisher Metric for off-the-grid Sparse Regularization Clarice Poon , Nicolas Keriven , Gabriel Peyré International Conference on Artificial Intelligence and Statistics (AISTATS), 2019, Naha, Japan Communication dans un congrès hal-02367999v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Large-Scale High-Dimensional Clustering with Fast Sketching Antoine Chatalic , Rémi Gribonval , Nicolas Keriven ICASSP 2018 - IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Canada. pp.4714-4718, ⟨10.1109/ICASSP.2018.8461328⟩ Communication dans un congrès hal-01701121v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Supervised learning of analysis-sparsity priors with automatic differentiation Hashem Ghanem , Joseph Salmon , Nicolas Keriven , Samuel Vaiter IEEE Signal Processing Letters, 2023, 30, pp.339-343. ⟨10.1109/LSP.2023.3244511⟩ Article dans une revue hal-03518852v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Convergence and Stability of Graph Convolutional Networks on Large Random Graphs Nicolas Keriven , Alberto Bietti , Samuel Vaiter NeurIPS 2020 - 34th Conference on Neural Information Processing Systems, Dec 2020, Vancouver (virtual), Canada Communication dans un congrès hal-02976711v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Stability of Entropic Wasserstein Barycenters and application to random geometric graphs Marc Theveneau , Nicolas Keriven GRETSI 2023 - XXIXème Colloque Francophone de Traitement du Signal et des Images, GRETSI - Groupe de Recherche en Traitement du Signal et des Images, Aug 2023, Grenoble, France. pp.1-7 Communication dans un congrès hal-03852485v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding Nicolas Keriven , Samuel Vaiter NeurIPS 2023 - 37th Annual Conference on Neural Information Processing Systems, Dec 2023, New-Orleans, United States. pp.1-28 Communication dans un congrès hal-04103771v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		The Graphical Nadaraya-Watson Estimator in Latent Position Models Martin Gjorgjevski , Nicolas Keriven , Simon Barthelme , Yohann de Castro GRETSI 2023 - XXIXème Colloque Francophone de Traitement du Signal et des Images, GRETSI - Groupe de Recherche en Traitement du Signal et des Images, Aug 2023, Grenoble, France. pp.1-4 Communication dans un congrès hal-04241086v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Compressive Statistical Learning with Random Feature Moments Rémi Gribonval , Gilles Blanchard , Nicolas Keriven , Yann Traonmilin Mathematical Statistics and Learning, 2021, 3 (2), pp.113-164. ⟨10.4171/msl/20⟩ Article dans une revue hal-01544609v5	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling Rémi Gribonval , Gilles Blanchard , Nicolas Keriven , Yann Traonmilin Mathematical Statistics and Learning, 2021, 3 (2), pp.165-257. ⟨10.4171/msl/21⟩ Article dans une revue hal-02536818v3	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		NEWMA: a new method for scalable model-free online change-point detection Nicolas Keriven , Damien Garreau , Iacopo Poli IEEE Transactions on Signal Processing, 2020, 68, pp.3515 - 3528. ⟨10.1109/TSP.2020.2990597⟩ Article dans une revue hal-02484988v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Compressive K-means Nicolas Keriven , Nicolas Tremblay , Yann Traonmilin , Rémi Gribonval ICASSP 2017 - IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2017, New Orleans, United States Communication dans un congrès hal-01386077v4	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Convergence of Graph Neural Networks with generic aggregation functions on random graphs Matthieu Cordonnier , Nicolas Keriven , Nicolas Tremblay , Samuel Vaiter GRETSI 2023 - XXIXème Colloque Francophone de Traitement du Signal et des Images, GRETSI - Groupe de Recherche en Traitement du Signal et des Images, Aug 2023, Grenoble, France. pp.1-4 Communication dans un congrès hal-04373554v1	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Instance Optimal Decoding and the Restricted Isometry Property Nicolas Keriven , Rémi Gribonval 8th International Conference on New Computational Methods for Inverse Problems (NCMIP), May 2018, Cachan, France. pp.012002, ⟨10.1088/1742-6596/1131/1/012002⟩ Communication dans un congrès hal-01718411v2	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More
		Sketching for large-scale learning of mixture models Nicolas Keriven Machine Learning [stat.ML]. Université de Rennes, 2017. English. ⟨NNT : 2017REN1S055⟩ Thèse tel-01620815v2	Envoyer sur ORCID Exporter BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite Export avancé Partager Gmail Facebook X LinkedIn More

Filtrer vos résultats

On the Universality of Graph Neural Networks on Large Random Graphs

Fast Graph Kernel with Optical Random Features

Gradient scarcity with Bilevel Optimization for Graph Learning

Entropic Optimal Transport in Random Graphs

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

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

Universal Invariant and Equivariant Graph Neural Networks

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

Sketching for Large-Scale Learning of Mixture Models

Projections aléatoires pour l'apprentissage compressif

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

Sketching Datasets for Large-Scale Learning (long version)

SketchMLbox -- A MATLAB toolbox for large-scale mixture learning

Blind Source Separation Using Mixtures of Alpha-Stable Distributions

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

The geometry of off-the-grid compressed sensing

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

Large-Scale High-Dimensional Clustering with Fast Sketching

Supervised learning of analysis-sparsity priors with automatic differentiation

Convergence and Stability of Graph Convolutional Networks on Large Random Graphs

Stability of Entropic Wasserstein Barycenters and application to random geometric graphs

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

The Graphical Nadaraya-Watson Estimator in Latent Position Models

Compressive Statistical Learning with Random Feature Moments

Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling

NEWMA: a new method for scalable model-free online change-point detection

Compressive K-means

Convergence of Graph Neural Networks with generic aggregation functions on random graphs

Instance Optimal Decoding and the Restricted Isometry Property

Sketching for large-scale learning of mixture models