| Robust sparse analysis regularization S Vaiter, G Peyré, C Dossal, J Fadili IEEE Transactions on Information Theory 59 (4), 2001–2016, 2013 | 149 | 2013 |
| 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 | 147 | 2014 |
| 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 | 91 | 2020 |
| 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 | 73 | 2022 |
| 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 | 72 | 2020 |
| 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 | 72 | 2015 |
| Model consistency of partly smooth regularizers S Vaiter, G Peyré, J Fadili IEEE Transactions on Information Theory 64 (3), 1725-1737, 2017 | 62 | 2017 |
| 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 | 55 | 2017 |
| 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 | 43 | 2012 |
| 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 | 40 | 2017 |
| Low complexity regularization of linear inverse problems S Vaiter, G Peyré, J Fadili Sampling Theory, a Renaissance: Compressive Sensing and Other Developments …, 2015 | 37 | 2015 |
| 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 | 34 | 2022 |
| 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 | 33 | 2022 |
| 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 | 28 | 2021 |
| Dual extrapolation for sparse glms M Massias, S Vaiter, A Gramfort, J Salmon Journal of Machine Learning Research 21 (234), 1-33, 2020 | 27 | 2020 |
| Accelerated alternating descent methods for Dykstra-like problems A Chambolle, P Tan, S Vaiter Journal of Mathematical Imaging and Vision 59, 481-497, 2017 | 25 | 2017 |
| 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 | 21 | 2012 |
| Stable recovery with analysis decomposable priors MJ Fadili, G Peyré, S Vaiter, C Deledalle, J Salmon arXiv preprint arXiv:1304.4407, 2013 | 20 | 2013 |
| Automatic differentiation of nonsmooth iterative algorithms J Bolte, E Pauwels, S Vaiter Advances in Neural Information Processing Systems 35, 26404-26417, 2022 | 19 | 2022 |
| The Degrees of Freedom of the Group Lasso S Vaiter, C Deledalle, G Peyré, J Fadili, C Dossal arXiv preprint arXiv:1205.1481, 2012 | 19 | 2012 |
Gabriel PeyréCNRS, DMA, Ecole Normale SupérieureVerified email at ens.fr
