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 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 NeurIPS (Spotlight), 2020. Pdf

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

portfolio

Portfolio item number 1 .

Short description of portfolio item number 1

Portfolio item number 2 .

Short description of portfolio item number 2

preprint

Nicolas Keriven, Samuel Vaiter. Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model . In arXiv Preprint, 2020. Pdf

Clarice Poon, Nicolas Keriven, Gabriel Peyré. The geometry of off-the-grid compressed sensing . In Preprint, 2020. Pdf

Nicolas Keriven, Alberto Bietti, Samuel Vaiter. On the Universality of Graph Neural Networks on Large Random Graphs . In preprint, 2021. 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

teaching

Teaching experience 1

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Teaching experience 2

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This is a description of a teaching experience. You can use markdown like any other post.