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Samuel Vaiter
Samuel Vaiter
CNRS Researcher
Verified email at math.cnrs.fr - Homepage
Title
Cited by
Cited by
Year
Robust sparse analysis regularization
S Vaiter, G Peyré, C Dossal, J Fadili
IEEE Transactions on Information Theory 59 (4), 2001–2016, 2013
1482013
Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection
CA Deledalle, S Vaiter, J Fadili, G Peyré
SIAM Journal on Imaging Sciences 7 (4), 2448-2487, 2014
1442014
Convergence and stability of graph convolutional networks on large random graphs
N Keriven, A Bietti, S Vaiter
Advances in Neural Information Processing Systems 33, 21512-21523, 2020
852020
Model selection with low complexity priors
S Vaiter, M Golbabaee, J Fadili, G Peyré
Information and Inference: A Journal of the IMA 4 (3), 230-287, 2015
712015
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
M Dagréou, P Ablin, S Vaiter, T Moreau
Advances in Neural Information Processing Systems 35, 26698-26710, 2022
692022
Implicit differentiation of lasso-type models for hyperparameter optimization
Q Bertrand, Q Klopfenstein, M Blondel, S Vaiter, A Gramfort, J Salmon
International Conference on Machine Learning, 810-821, 2020
662020
Model consistency of partly smooth regularizers
S Vaiter, G Peyré, J Fadili
IEEE Transactions on Information Theory 64 (3), 1725-1737, 2017
612017
The degrees of freedom of partly smooth regularizers
S Vaiter, C Deledalle, J Fadili, G Peyré, C Dossal
Annals of the Institute of Statistical Mathematics 69, 791-832, 2017
522017
Local behavior of sparse analysis regularization: Applications to risk estimation
S Vaiter, C Deledalle, G Peyré, C Dossal, J Fadili
Applied and Computational Harmonic Analysis 35 (3), 433-451, 2012
422012
Clear: Covariant least-square refitting with applications to image restoration
CA Deledalle, N Papadakis, J Salmon, S Vaiter
SIAM Journal on Imaging Sciences 10 (1), 243-284, 2017
402017
Low complexity regularization of linear inverse problems
S Vaiter, G Peyré, J Fadili
Sampling Theory, a Renaissance: Compressive Sensing and Other Developments …, 2015
362015
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning
Q Bertrand, Q Klopfenstein, M Massias, M Blondel, S Vaiter, A Gramfort, ...
Journal of Machine Learning Research 23 (149), 1-43, 2022
312022
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
T Moreau, M Massias, A Gramfort, P Ablin, PA Bannier, B Charlier, ...
Advances in Neural Information Processing Systems 35, 25404-25421, 2022
302022
On the universality of graph neural networks on large random graphs
N Keriven, A Bietti, S Vaiter
Advances in Neural Information Processing Systems 34, 6960-6971, 2021
272021
Dual extrapolation for sparse glms
M Massias, S Vaiter, A Gramfort, J Salmon
Journal of Machine Learning Research 21 (234), 1-33, 2020
272020
Accelerated alternating descent methods for Dykstra-like problems
A Chambolle, P Tan, S Vaiter
Journal of Mathematical Imaging and Vision 59, 481-497, 2017
242017
The degrees of freedom of the Group Lasso for a General Design
S Vaiter, C Deledalle, G Peyré, J Fadili, C Dossal
arXiv preprint arXiv:1212.6478, 2012
212012
Stable recovery with analysis decomposable priors
MJ Fadili, G Peyré, S Vaiter, C Deledalle, J Salmon
arXiv preprint arXiv:1304.4407, 2013
202013
The Degrees of Freedom of the Group Lasso
S Vaiter, C Deledalle, G Peyré, J Fadili, C Dossal
arXiv preprint arXiv:1205.1481, 2012
192012
Risk estimation for matrix recovery with spectral regularization
CA Deledalle, S Vaiter, G Peyré, J Fadili, C Dossal
arXiv preprint arXiv:1205.1482, 2012
182012
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