Nicolas Keriven

Nicolas Keriven

CNRS researcher in signal processing and machine learning, SIROCCO 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

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

portfolio

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Portfolio item number 2 .

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preprint

Hashem Ghanem, Samuel Vaiter, Nicolas Keriven. Gradient scarcity with Bilevel Optimization for Graph Learning . In arXiv Preprint, 2023. Pdf

Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter. Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs . In arXiv Preprint, 2023. 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

Graph Neural Networks on Large Random Graphs: Convergence, Stability, Universality . Aug 2021-Nov 2022 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

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

teaching

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Teaching experience 2

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