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Samuel Vaiter
Samuel Vaiter
CNRS Researcher
Adresse e-mail validée de math.cnrs.fr - Page d'accueil
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Année
Robust sparse analysis regularization
S Vaiter, G Peyré, C Dossal, J Fadili
IEEE Transactions on Information Theory 59 (4), 2001–2016, 2013
1432013
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
1192014
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
602015
Model consistency of partly smooth regularizers
S Vaiter, G Peyré, J Fadili
IEEE Transactions on Information Theory 64 (3), 1725-1737, 2017
522017
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 (4), 791-832, 2017
402017
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
392017
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
382020
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
372012
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
322020
Low complexity regularization of linear inverse problems
S Vaiter, G Peyré, J Fadili
Sampling Theory, a Renaissance, 103-153, 2015
292015
Dual extrapolation for sparse generalized linear models
M Massias, S Vaiter, A Gramfort, J Salmon
Journal of Machine Learning Research 21 (234), 1-33, 2020
202020
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
202012
Stable recovery with analysis decomposable priors
MJ Fadili, G Peyré, S Vaiter, C Deledalle, J Salmon
arXiv preprint arXiv:1304.4407, 2013
192013
Accelerated alternating descent methods for Dykstra-like problems
A Chambolle, P Tan, S Vaiter
Journal of Mathematical Imaging and Vision 59 (3), 481-497, 2017
182017
Unbiased risk estimation for sparse analysis regularization
C Deledalle, S Vaiter, G Peyré, J Fadili, C Dossal
Proc. ICIP'12, 2012
182012
The Degrees of Freedom of the Group Lasso
S Vaiter, C Deledalle, G Peyré, J Fadili, C Dossal
arXiv preprint arXiv:1205.1481, 2012
172012
Risk estimation for matrix recovery with spectral regularization
CA Deledalle, S Vaiter, G Peyré, J Fadili, C Dossal
arXiv preprint arXiv:1205.1482, 2012
172012
Proximal Splitting Derivatives for Risk Estimation
C Deledalle, S Vaiter, G Peyré, J Fadili, C Dossal
Proc. NCMIP'12, 2012
132012
Sparse and smooth: Improved guarantees for spectral clustering in the dynamic stochastic block model
N Keriven, S Vaiter
Electronic Journal of Statistics 16 (1), 1330-1366, 2022
112022
Automated data-driven selection of the hyperparameters for total-variation-based texture segmentation
B Pascal, S Vaiter, N Pustelnik, P Abry
Journal of Mathematical Imaging and Vision 63 (7), 923-952, 2021
102021
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