Structured sparsity using backwards elimination for Automatic Music Transcription. - Nicolas Keriven; Ken O'Hanlon; Mark D. Plumbley (MLSP, 2013)

Sketching for large-scale learning of mixture models. - Nicolas Keriven; Anthony Bourrier; Rémi Gribonval; Patrick Pérez (ICASSP, 2016)

Non-Negative Group Sparsity with Subspace Note Modelling for Polyphonic Transcription. - Ken O'Hanlon; Hidehisa Nagano; Nicolas Keriven; Mark D. Plumbley (IEEE ACM Trans. Audio Speech Lang. Process., 2016)

Compressive K-means. - Nicolas Keriven; Nicolas Tremblay; Yann Traonmilin; Rémi Gribonval (ICASSP, 2017)

Compressive Statistical Learning with Random Feature Moments. - Rémi Gribonval; Gilles Blanchard; Nicolas Keriven; Yann Traonmilin (CoRR, 2017)

Sketching for Large-Scale Learning of Mixture Models. (Apprentissage de modèles de mélange à large échelle par Sketching). - Nicolas Keriven (2017)

Large-Scale High-Dimensional Clustering with Fast Sketching. - Antoine Chatalic; Rémi Gribonval; Nicolas Keriven (ICASSP, 2018)

Blind Source Separation Using Mixtures of Alpha-Stable Distributions. - Nicolas Keriven; Antoine Deleforge; Antoine Liutkus (ICASSP, 2018)

A Dual Certificates Analysis of Compressive Off-the-Grid Recovery. - Clarice Poon; Nicolas Keriven; Gabriel Peyré (CoRR, 2018)

Instance Optimal Decoding and the Restricted Isometry Property. - Nicolas Keriven; Rémi Gribonval (CoRR, 2018)

Support Localization and the Fisher Metric for off-the-grid Sparse Regularization. - Clarice Poon; Nicolas Keriven; Gabriel Peyré (AISTATS, 2019)

Universal Invariant and Equivariant Graph Neural Networks. - Nicolas Keriven; Gabriel Peyré (NeurIPS, 2019)

Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. - Nicolas Keriven; Alberto Bietti; Samuel Vaiter (NeurIPS, 2020)

Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model. - Nicolas Keriven; Samuel Vaiter (CoRR, 2020)

Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling. - Rémi Gribonval; Gilles Blanchard; Nicolas Keriven; Yann Traonmilin (CoRR, 2020)

Sketching Datasets for Large-Scale Learning (long version). - Rémi Gribonval; Antoine Chatalic; Nicolas Keriven; Vincent Schellekens; Laurent Jacques; Philip Schniter (CoRR, 2020)

NEWMA: A New Method for Scalable Model-Free Online Change-Point Detection. - Nicolas Keriven; Damien Garreau; Iacopo Poli (IEEE Trans. Signal Process., 2020)

Fast Graph Kernel with Optical Random Features. - Hashem Ghanem; Nicolas Keriven; Nicolas Tremblay (ICASSP, 2021)

On the Universality of Graph Neural Networks on Large Random Graphs. - Nicolas Keriven; Alberto Bietti; Samuel Vaiter (NeurIPS, 2021)

Sketching Data Sets for Large-Scale Learning: Keeping only what you need. - Rémi Gribonval; Antoine Chatalic; Nicolas Keriven; Vincent Schellekens; Laurent Jacques; Philip Schniter (IEEE Signal Process. Mag., 2021)

Not too little, not too much: a theoretical analysis of graph (over)smoothing. - Nicolas Keriven (NeurIPS, 2022)

Stability of Entropic Wasserstein Barycenters and application to random geometric graphs. - Marc Theveneau; Nicolas Keriven (CoRR, 2022)

What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. - Nicolas Keriven; Samuel Vaiter (NeurIPS, 2023)

Gradient scarcity with Bilevel Optimization for Graph Learning. - Hashem Ghanem; Samuel Vaiter; Nicolas Keriven (CoRR, 2023)

Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs. - Matthieu Cordonnier; Nicolas Keriven; Nicolas Tremblay; Samuel Vaiter (CoRR, 2023)

Supervised Learning of Analysis-Sparsity Priors With Automatic Differentiation. - Hashem Ghanem; Joseph Salmon; Nicolas Keriven; Samuel Vaiter (IEEE Signal Process. Lett., 2023)

The Geometry of Off-the-Grid Compressed Sensing. - Clarice Poon; Nicolas Keriven; Gabriel Peyré (Found. Comput. Math., 2023)

Entropic Optimal Transport on Random Graphs. - Nicolas Keriven (SIAM J. Math. Data Sci., 2023)

Seeking Universal Approximation for Continuous Limits of Graph Neural Networks on Large Random Graphs. - Matthieu Cordonnier; Nicolas Keriven; Nicolas Tremblay; Samuel Vaiter (IEEECONF, 2024)

Graph Coarsening with Message-Passing Guarantees. - Antonin Joly; Nicolas Keriven (NeurIPS, 2024)

Node Regression on Latent Position Random Graphs via Local Averaging. - Martin Gjorgjevski; Nicolas Keriven; Simon Barthelmé; Yohann De Castro (CoRR, 2024)

Gradient Scarcity in Graph Learning with Bilevel Optimization. - Hashem Ghanem; Samuel Vaiter; Nicolas Keriven (Trans. Mach. Learn. Res., 2024)

Backward Oversmoothing: why is it hard to train deep Graph Neural Networks? - Nicolas Keriven (CoRR, 2025)

Taxonomy of reduction matrices for Graph Coarsening. - Antonin Joly; Nicolas Keriven; Aline Roumy (CoRR, 2025)