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2020 – today
- 2024
[j7]Hashem Ghanem, Samuel Vaiter, Nicolas Keriven:
Gradient Scarcity in Graph Learning with Bilevel Optimization. Trans. Mach. Learn. Res. 2024 (2024)- [i22]Antonin Joly, Nicolas Keriven:
Graph Coarsening with Message-Passing Guarantees. CoRR abs/2405.18127 (2024) - 2023
- [j6]Clarice Poon, Nicolas Keriven, Gabriel Peyré:
The Geometry of Off-the-Grid Compressed Sensing. Found. Comput. Math. 23(1): 241-327 (2023) - [j5]Nicolas Keriven
:
Entropic Optimal Transport on Random Graphs. SIAM J. Math. Data Sci. 5(4): 1028-1050 (2023) - [j4]Hashem Ghanem, Joseph Salmon, Nicolas Keriven, Samuel Vaiter:
Supervised Learning of Analysis-Sparsity Priors With Automatic Differentiation. IEEE Signal Process. Lett. 30: 339-343 (2023) - [c12]Nicolas Keriven, Samuel Vaiter:
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. NeurIPS 2023 - [i21]Hashem Ghanem, Samuel Vaiter, Nicolas Keriven:
Gradient scarcity with Bilevel Optimization for Graph Learning. CoRR abs/2303.13964 (2023) - [i20]Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter:
Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs. CoRR abs/2304.11140 (2023) - [i19]Nicolas Keriven, Samuel Vaiter:
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding. CoRR abs/2305.14814 (2023) - 2022
- [c11]Nicolas Keriven:
Not too little, not too much: a theoretical analysis of graph (over)smoothing. NeurIPS 2022 - [i18]Nicolas Keriven:
Entropic Optimal Transport in Random Graphs. CoRR abs/2201.03949 (2022) - [i17]Nicolas Keriven:
Not too little, not too much: a theoretical analysis of graph (over)smoothing. CoRR abs/2205.12156 (2022) - [i16]Marc Theveneau, Nicolas Keriven:
Stability of Entropic Wasserstein Barycenters and application to random geometric graphs. CoRR abs/2210.10535 (2022) - 2021
- [j3]Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter:
Sketching Data Sets for Large-Scale Learning: Keeping only what you need. IEEE Signal Process. Mag. 38(5): 12-36 (2021) - [c10]Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay:
Fast Graph Kernel with Optical Random Features. ICASSP 2021: 3575-3579 - [c9]Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
On the Universality of Graph Neural Networks on Large Random Graphs. NeurIPS 2021: 6960-6971 - [i15]Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
On the Universality of Graph Neural Networks on Large Random Graphs. CoRR abs/2105.13099 (2021) - 2020
- [j2]Nicolas Keriven, Damien Garreau, Iacopo Poli:
NEWMA: A New Method for Scalable Model-Free Online Change-Point Detection. IEEE Trans. Signal Process. 68: 3515-3528 (2020) - [c8]Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. NeurIPS 2020 - [i14]Nicolas Keriven, Samuel Vaiter:
Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model. CoRR abs/2002.02892 (2020) - [i13]Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin:
Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling. CoRR abs/2004.08085 (2020) - [i12]Nicolas Keriven, Alberto Bietti, Samuel Vaiter:
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. CoRR abs/2006.01868 (2020) - [i11]Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter:
Sketching Datasets for Large-Scale Learning (long version). CoRR abs/2008.01839 (2020) - [i10]Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay:
Fast Graph Kernel with Optical Random Features. CoRR abs/2010.08270 (2020)
2010 – 2019
- 2019
- [c7]Clarice Poon, Nicolas Keriven, Gabriel Peyré:
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization. AISTATS 2019: 1341-1350 - [c6]Nicolas Keriven, Gabriel Peyré:
Universal Invariant and Equivariant Graph Neural Networks. NeurIPS 2019: 7090-7099 - [i9]Nicolas Keriven, Gabriel Peyré:
Universal Invariant and Equivariant Graph Neural Networks. CoRR abs/1905.04943 (2019) - 2018
- [c5]Nicolas Keriven, Antoine Deleforge, Antoine Liutkus:
Blind Source Separation Using Mixtures of Alpha-Stable Distributions. ICASSP 2018: 771-775 - [c4]Antoine Chatalic, Rémi Gribonval, Nicolas Keriven:
Large-Scale High-Dimensional Clustering with Fast Sketching. ICASSP 2018: 4714-4718 - [i8]Clarice Poon, Nicolas Keriven, Gabriel Peyré:
A Dual Certificates Analysis of Compressive Off-the-Grid Recovery. CoRR abs/1802.08464 (2018) - [i7]Nicolas Keriven, Rémi Gribonval:
Instance Optimal Decoding and the Restricted Isometry Property. CoRR abs/1802.09905 (2018) - [i6]Nicolas Keriven, Damien Garreau, Iacopo Poli:
NEWMA: a new method for scalable model-free online change-point detection. CoRR abs/1805.08061 (2018) - [i5]Clarice Poon, Nicolas Keriven, Gabriel Peyré:
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization. CoRR abs/1810.03340 (2018) - 2017
- [b1]Nicolas Keriven:
Sketching for Large-Scale Learning of Mixture Models. (Apprentissage de modèles de mélange à large échelle par Sketching). University of Rennes 1, France, 2017 - [c3]Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval:
Compressive K-means. ICASSP 2017: 6369-6373 - [i4]Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin:
Compressive Statistical Learning with Random Feature Moments. CoRR abs/1706.07180 (2017) - [i3]Nicolas Keriven, Antoine Deleforge, Antoine Liutkus:
Blind Source Separation Using Mixtures of Alpha-Stable Distributions. CoRR abs/1711.04460 (2017) - 2016
- [j1]Ken O'Hanlon, Hidehisa Nagano, Nicolas Keriven, Mark D. Plumbley:
Non-Negative Group Sparsity with Subspace Note Modelling for Polyphonic Transcription. IEEE ACM Trans. Audio Speech Lang. Process. 24(3): 530-542 (2016) - [c2]Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez:
Sketching for large-scale learning of mixture models. ICASSP 2016: 6190-6194 - [i2]Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez:
Sketching for Large-Scale Learning of Mixture Models. CoRR abs/1606.02838 (2016) - [i1]Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval:
Compressive K-means. CoRR abs/1610.08738 (2016) - 2013
- [c1]Nicolas Keriven, Ken O'Hanlon, Mark D. Plumbley:
Structured sparsity using backwards elimination for Automatic Music Transcription. MLSP 2013: 1-6
Coauthor Index
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last updated on 2024-08-10 01:28 CEST by the dblp team
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