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 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 