Nicolas Keriven

Nicolas Keriven

CNRS researcher in signal processing and machine learning, COMPACT team, IRISA.
nicolas.keriven [at] cnrs [dot] fr

Posts by Collection

conference

Nicolas Keriven, Ken O’Hanlon, Mark Plumbley. Structured sparsity using backwards elimination for automatic music transcription . In MLSP, 2013. Pdf

Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez. Sketching for Large-Scale Learning of Mixture Model. . In ICASSP, 2016. Pdf, Code

Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval. Compressive k-means . In ICASSP, 2017. Pdf

Nicolas Keriven, Antoine Deleforge, Antoine Liutkus. Blind Source Separation Using Mixtures of Alpha-Stable Distributions . In ICASSP, 2018. Pdf, Code

Antoine Chatalic, Rémi Gribonval, Nicolas Keriven. Large-Scale High-Dimensional Clustering with Fast Sketching . In ICASSP, 2018. Pdf

Nicolas Keriven, Rémi Gribonval. Instance Optimal Decoding and the Restricted Isometry Property . In NCMIP, 2018. Pdf

Clarice Poon, Nicolas Keriven, Gabriel Peyré. Support Localization and the Fisher Metric for off-the-grid Sparse Regularization . In International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. Pdf

Antoine Chatalic, Nicolas Keriven, Rémi Gribonval. Projections aléatoires pour l’apprentissage compressif . In GRETSI, 2019. Pdf

Nicolas Keriven, Gabriel Peyré. Universal Invariant and Equivariant Graph Neural Networks . In Advances in Neural Information Processing Systems (NeurIPS), 2019. Pdf

Nicolas Keriven, Samuel Vaiter. Partitionnement spectral et modèle à blocs stochastique dynamique: parcimonie et régularité . In 52èmes Journées de Statistique de la Société Française de Statistique (SFdS), 2020. Pdf

Nicolas Keriven, Alberto Bietti, Samuel Vaiter. Convergence and Stability of Graph Convolutional Networks on Large Random Graphs . In Advances in Neural Information Processing Systems (NeurIPS) Spotlight, 2020. Pdf

Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay. Fast Graph Kernel with Optical Random Features . In ICASSP 2021, 2021. Pdf

Nicolas Keriven, Alberto Bietti, Samuel Vaiter. On the Universality of Graph Neural Networks on Large Random Graphs . In Advances in Neural Information Processing Systems (NeurIPS), 2021. Pdf

Martin Gjorgjevski, Nicolas Keriven, Simon Barthelmé, Yohann de Castro. The Graphical Nadaraya-Watson Estimator in Latent Position Models . In GRETSI 2023, 2022. Pdf

Marc Theveneau, Nicolas Keriven. Stability of Entropic Wasserstein Barycenters and application to random geometric graphs . In GRETSI 2023, 2022. Pdf

Nicolas Keriven. Not too little, not too much: a theoretical analysis of graph (over)smoothing . In Advances in Neural Information Processing Systems (NeurIPS) 2022 Oral, 2022. Pdf

Nicolas Keriven, Samuel Vaiter. What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding . In Advances in Neural Informations Processing Systems (NeurIPS), 2023. Pdf

Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter. Seeking universal approximation for continuous limits of graph neural networks on large random graphs . In Asilomar Conference on Signals, Systems, and Computers, 2024. Pdf

Antonin Joly, Nicolas Keriven. Graph Coarsening with Message-Passing Guarantees . In Advances in Neural Information Processing Systems (NeurIPS), 2024. Pdf

journal

Ken O’Hanlon, Hidehisa Nagano, Nicolas Keriven, Mark Plumbley. Non-negative group sparsity with subspace note modelling for polyphonic transcription . In IEEE/ACM Transactions on Audio, Speech, and Language Processing volume 24, issue 3, pp. 530-542, 2016. Pdf

Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez. Sketching for Large-Scale Learning of Mixture Models . In Information and Inference: a Journal of the IMA, vol. 7, issue 3, pp. 447-508, 2018. Pdf

Nicolas Keriven, Damien Garreau, Iacopo Poli. NEWMA: a new method for scalable model-free online change-point detection . In IEEE, Transactions on Signal Processing, vol. 68, pp. 3515-3528, 2020. Pdf

Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin. Compressive Statistical Learning with Random Feature Moments . In Mathematical Statistics and Learning, EMS Publishing House, 2021, 3 (2), pp.113-164., 2021. Pdf

Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin. Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling . In Mathematical Statistics and Learning, EMS Publishing House, 2021, 3 (2), pp.165-257., 2021. Pdf

Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter. Sketching Datasets for Large-Scale Learning . In IEEE Signal Processing Magazine, 38 (5), Sept. 2021, pp.12-36., 2021. Pdf

Clarice Poon, Nicolas Keriven, Gabriel Peyré. The geometry of off-the-grid compressed sensing . In Foundations of Computational Mathematics, 2021. Pdf

Nicolas Keriven, Samuel Vaiter. Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model . In Electronic Journal of Statistics 16 (1), 1330 - 1366, 2022. Pdf

Hashem Ghanem, Joseph Salmon, Nicolas Keriven, Samuel Vaiter. Supervised learning of analysis-sparsity priors with automatic differentiation . In IEEE Signal Processing Letters, 2023. Pdf

Nicolas Keriven. Entropic Optimal Transport in Random Graphs . In SIAM Journal on Mathematics of Data Science (SIMODS) (In Press), 2023. Pdf

Hashem Ghanem, Samuel Vaiter, Nicolas Keriven. Gradient scarcity with Bilevel Optimization for Graph Learning . In Transactions in Machine Learning Research (TMLR) Featured Certification, 2024. Pdf

Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter. Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs . In Journal of Machine Learning Research (JMLR), 2024. Pdf

portfolio

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Portfolio item number 2 .

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preprint

Martin Gjorgjevski, Nicolas Keriven, Simon Barthelmé, Yohann De Castro. Node Regression on Latent Position Random Graphs via Local Averaging . In arXiv Preprint, 2024. Pdf

Nicolas Keriven. Backward Oversmoothing: why is it hard to train deep Graph Neural Networks? . In arXiv Preprint, 2025. Pdf

Antonin Joly, Nicolas Keriven, Aline Roumy. Taxonomy of reduction matrices for Graph Coarsening . In arXiv Preprint, 2025. Pdf

talks

Compressive Gaussian Mixture Estimation by Orthogonal Matching Pursuit with Replacement . SPARS, Jul. 2015, Cambridge.

Sketching for Large-Scale Learning of Mixture model . International Matheon Conference on Compressed Sensing and its Applications, Dec. 2015, Berlin.

Sketching for Large-Scale Learning of Mixture model . ICASSP, Shanghaï, Mar. 2016.

Sketching for Large-Scale Learning of Mixture model . Journée du GdR Isis: Algorithmes gloutons pour l'optimisation sous contrainte de parcimonie, Paris, Jun. 2016. Slides

Sketching for Large-Scale Learning of Mixture model . Ecole d'été Peyresq, Peyresq, Jul. 2016. Slides

Sketching for Large-Scale Learning of Mixture model . Jan. 2017 - Mar. 2017 Slides
- Thesis pre-defense, IRISA, Rennes
- Séminaires de Statistiques de l’IRMAR, IRMAR, Rennes
- IdCOM Seminars, University of Edinburgh, Edinburgh
- Journée Classification MimeTIC, IRISA, Rennes.

Random Moments for Sketched Mixture Learning . SPARS, Jun. 2017, Lisbon. Slides, Abstract
  • Best Student Paper Award, SPARS 2017.

Spike super-resolution with random Fourier sampling . SPARS, Jun. 2017, Lisbon. Slides, Abstract

Sketching for Large-Scale Learning of Mixture model . Nov. 2017 - Dec 2017. Slides
- Gatsby Computational Neuroscience UCL, London
- CFM-ENS Data Science chair Annual Conference, ENS, Paris
- The Future of Random Projections : a mini-workshop, IPGG, Paris. Video

Sketched Learning from Random Features Moments . Telecom-Paristech, Apr. 2018, Paris. Slides

A Dual Certificate Analysis of Compressive Off-the-grid Recovery . Journee thématique du GDR MIA: Parcimonie et applications, IMB, May 2018, Talence, France. Slides

NEWMA: a new method for scalable model-free online change-point detection . Séminaire SPOC, June 2018, Dijon, France. Slides

Sketched Learning from Random Feature Moments . International Symposium on Math. Programming (ISMP), July 2018, Bordeaux, France. Slides

Fisher metric, support stability and optimal number of measurements in compressive off the grid recovery . Oct. 2018 - Nov. 2018 Slides
- Conférence OMS, Institut de Mathématiques de Toulouse, Toulouse
- Séminaire S3, CentraleSupélec, L2S, Paris
- Séminaire de l'équipe Sigma, CRIStAL, Lille
- Ecole Normale Supérieure de Lyon, Laboratoire de Physique, Lyon - THOTH team seminar, INRIA Rhône-Alpes, Grenoble

Scalable, online change point detection with NEWMA . Nov. 2018 - Jan. 2019 Slides
- CICS team seminar, GIPSA-lab, Grenoble
- SELECT team seminar, INRIA Saclay, Orsay
- Séminaire Probabilités et Statistiques, Laboratoire Paul Painlevé, Lille
- Séminaire Image Optimisation et Probabilités, Institut de Mathématiques de Bordeaux
- CERMICS, ENPC, Marne-la-vallée
- Equipe DANTE, ENS Lyon

A short introduction to graphon . Mar. 2020 Slides
GAIA team seminar

Sparse and Smooth: Spectral Clustering in the dynamic SBM . Dec. 2020 Slides
iTWIST 2020

Convergence and Stability of GCNs on Large Random Graphs . Dec. 2020 Slides
NeurIPS (Spotlight)

Graph Neural Networks: Introduction, some theoretical properties . Jan. 2021 Slides
- Data Science Master Seminar, Lille
- GAIA team seminar

Not too little, not too much: a theoretical analysis of graph (over)smoothing . Dec. 2022 Slides
- NeurIPS22 (Oral)
- Learning on Graphs Conference 22 (Spotlight)

Entropic Optimal Transport and Wasserstein Barycenters in Random Graphs . Nov. 2021 - Mar. 2023 Slides
- GdR ISIS - MIA day: Optimal Transport and Statistical Learning
- One World MINDS Seminar

Graph Neural Networks on Large Random Graphs: Convergence, Stability, Universality . 2021-2024 Slides
- ICIAM 2023: Mini-symposium on Geometric methods in machine learning and data analysis
- Journées Géosto MIA 2022
- Séminaires MLSP, ENS Lyon
- SIAM Imaging Sciences 2022: Early Career Prize Lecture
- Journée MAS 2020
- GdR MIA Day: High-Dimensional Data Analysis
- Séminaires LJAD (Nice)
- SIAM/IS Early Career Prize
- ML-MTP : Machine Learning in Montpellier, Theory and Practice
- Séminaire ENS Lyon, DANTE

teaching

Teaching experience 1

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Teaching experience 2

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