Publications
Thesis: Sketching for Large-Scale Learning of Mixture Models [Pdf]
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 Journal
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 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 Nicolas Keriven. Entropic Optimal Transport in Random Graphs . In SIAM Journal on Mathematics of Data Science (SIMODS) (In Press), 2023. 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, 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 Clarice Poon, Nicolas Keriven, Gabriel Peyré. The geometry of off-the-grid compressed sensing . In Foundations of Computational Mathematics, 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 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, 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 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 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 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 Conference
Antonin Joly, Nicolas Keriven. Graph Coarsening with Message-Passing Guarantees . In Advances in Neural Information Processing Systems (NeurIPS), 2024. 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 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 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 Marc Theveneau, Nicolas Keriven. Stability of Entropic Wasserstein Barycenters and application to random geometric graphs . In GRETSI 2023, 2022. Pdf Martin Gjorgjevski, Nicolas Keriven, Simon Barthelmé, Yohann de Castro. The Graphical Nadaraya-Watson Estimator in Latent Position Models . In GRETSI 2023, 2022. 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 Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay. Fast Graph Kernel with Optical Random Features . In ICASSP 2021, 2021. 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 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, Gabriel Peyré. Universal Invariant and Equivariant Graph Neural Networks . In Advances in Neural Information Processing Systems (NeurIPS), 2019. Pdf Antoine Chatalic, Nicolas Keriven, Rémi Gribonval. Projections aléatoires pour l’apprentissage compressif . In GRETSI, 2019. 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 Nicolas Keriven, Rémi Gribonval. Instance Optimal Decoding and the Restricted Isometry Property . In NCMIP, 2018. Pdf Antoine Chatalic, Rémi Gribonval, Nicolas Keriven. Large-Scale High-Dimensional Clustering with Fast Sketching . In ICASSP, 2018. Pdf Nicolas Keriven, Antoine Deleforge, Antoine Liutkus. Blind Source Separation Using Mixtures of Alpha-Stable Distributions . In ICASSP, 2018. Pdf, Code Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval. Compressive k-means . In ICASSP, 2017. Pdf Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez. Sketching for Large-Scale Learning of Mixture Model. . In ICASSP, 2016. Pdf, Code Nicolas Keriven, Ken O’Hanlon, Mark Plumbley. Structured sparsity using backwards elimination for automatic music transcription . In MLSP, 2013. Pdf 