Samuel Vaiter Publications de Samuel Vaiter

Publications

Article dans une revue (26)

Bilevel gradient methods and the Morse parametric qualification condition Jérôme Bolte,Quoc-Tung Le,Edouard Pauwels,Samuel Vaiter Mathematics of Operations Research, In press, 31 p Article dans une revue hal-04942322v3
Geometric and computational hardness of bilevel programming Jérôme Bolte,Quoc-Tung Le,Edouard Pauwels,Samuel Vaiter Mathematical Programming, Series A, In press, 32 p Article dans une revue hal-04649435v2
Risk Estimate under a Time-Varying Autoregressive Model for Data-Driven Reproduction Number Estimation Barbara Pascal,Samuel Vaiter Signal Processing, 2025, pp.110246 Article dans une revue hal-04705913v3
A theory of optimal convex regularization for low-dimensional recovery Yann Traonmilin,Rémi Gribonval,Samuel Vaiter Information and Inference, 2024, 13 (2) Article dans une revue hal-03467123v3
Gradient scarcity with Bilevel Optimization for Graph Learning Hashem Ghanem,Samuel Vaiter,Nicolas Keriven Transactions on Machine Learning Research Journal, 2024 Article dans une revue hal-04041721v1
Local linear convergence of proximal coordinate descent algorithm Quentin Klopfenstein,Quentin Bertrand,Alexandre Gramfort,Joseph Salmon,S. Vaiter Optimization Letters, 2024, 18, pp.135-154. ⟨10.1007/s11590-023-01976-z⟩ Article dans une revue hal-04308828v1
Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs Matthieu Cordonnier,Nicolas Keriven,Nicolas Tremblay,Samuel Vaiter Journal of Machine Learning Research, 2024, 25 (406), pp.1-49 Article dans une revue hal-04059402v3
The derivatives of Sinkhorn-Knopp converge Edouard Pauwels,Samuel Vaiter SIAM Journal on Optimization, 2023, 33 (3), ⟨10.48550/arXiv.2207.12717⟩ Article dans une revue hal-03736905v3
Supervised learning of analysis-sparsity priors with automatic differentiation Hashem Ghanem,Joseph Salmon,Nicolas Keriven,Samuel Vaiter IEEE Signal Processing Letters, 2023, 30, pp.339-343. ⟨10.1109/LSP.2023.3244511⟩ Article dans une revue hal-03518852v1
The Geometry of Sparse Analysis Regularization Xavier Dupuis,Samuel Vaiter SIAM Journal on Optimization, 2023, 33 (2), pp.842-867. ⟨10.1137/19M1271877⟩ Article dans une revue hal-02169356v2
Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model Nicolas Keriven,Samuel Vaiter Electronic Journal of Statistics , 2022, 16 (1), pp.1330-1366. ⟨10.1214/22-ejs1986⟩ Article dans une revue hal-02484970v1
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning Quentin Bertrand,Quentin Klopfenstein,Mathurin Massias,Mathieu Blondel,Samuel Vaiteret al. Journal of Machine Learning Research, 2022, 23 (1), pp.6680 - 6722 Article dans une revue hal-03228663v2
Linear support vector regression with linear constraints Quentin Klopfenstein,Samuel Vaiter Machine Learning, 2021, 110 (7), pp.1939-1974. ⟨10.1007/s10994-021-06018-2⟩ Article dans une revue hal-03303248v1
Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation Barbara Pascal,Samuel Vaiter,Nelly Pustelnik,Patrice Abry Journal of Mathematical Imaging and Vision, 2021, 63, pp.923-952. ⟨10.1007/s10851-021-01035-1⟩ Article dans une revue hal-03044181v1
Block based refitting in $\ell_{12}$ sparse regularisation Charles-Alban Deledalle,Nicolas Papadakis,Joseph Salmon,Samuel Vaiter Journal of Mathematical Imaging and Vision, 2021, 63, pp.216-236. ⟨10.1007/s10851-020-00993-2⟩ Article dans une revue hal-02330441v1
Dual Extrapolation for Sparse Generalized Linear Models Mathurin Massias,Samuel Vaiter,Alexandre Gramfort,Joseph Salmon Journal of Machine Learning Research, 2020, 21 (234), pp.1-33 Article dans une revue hal-02263500v1
Maximal Solutions of Sparse Analysis Regularization Abdessamad Barbara,Abderrahim Jourani,Samuel Vaiter Journal of Optimization Theory and Applications, 2019, 180 (2), pp.374-396. ⟨10.1007/s10957-018-1385-3⟩ Article dans une revue hal-01467965v1
Model Consistency of Partly Smooth Regularizers Samuel Vaiter,Gabriel Peyré,Jalal M. Fadili IEEE Transactions on Information Theory, 2018, 64 (3), pp.1725-1737. ⟨10.1109/TIT.2017.2713822⟩ Article dans une revue hal-01658847v1
Accelerated Alternating Descent Methods for Dykstra-like problems Antonin Chambolle,Pauline Tan,Samuel Vaiter Journal of Mathematical Imaging and Vision, 2017, 59 (3), pp.481-497. ⟨10.1007/s10851-017-0724-6⟩ Article dans une revue hal-01346532v1
The degrees of freedom of partly smooth regularizers Samuel Vaiter,Charles-Alban Deledalle,Jalal M. Fadili,Gabriel Peyré,Charles H Dossal Annals of the Institute of Statistical Mathematics, 2017, 69 (4), pp.791 - 832. ⟨10.1007/s10463-016-0563-z⟩ Article dans une revue hal-00981634v4
CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration Charles-Alban Deledalle,Nicolas Papadakis,Joseph Salmon,Samuel Vaiter SIAM Journal on Imaging Sciences, 2017, 10 (1), pp.243-284. ⟨10.1137/16M1080318⟩ Article dans une revue hal-01333295v3
A sharp oracle inequality for Graph-Slope Pierre C. Bellec,Joseph Salmon,Samuel Vaiter Electronic Journal of Statistics , 2017, 11 (2), pp.4851-4870. ⟨10.1214/17-EJS1364⟩ Article dans une revue hal-01544680v1
Model Selection with Low Complexity Priors Samuel Vaiter,Mohammad Golbabaee,Jalal M. Fadili,Gabriel Peyré Information and Inference, 2015, 52 p Article dans une revue hal-00842603v3
Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection Charles-Alban Deledalle,Samuel Vaiter,Jalal M. Fadili,Gabriel Peyré SIAM Journal on Imaging Sciences, 2014, 7 (4), pp.2448-2487 Article dans une revue hal-00987295v2
Robust Sparse Analysis Regularization Samuel Vaiter,Gabriel Peyré,Charles H Dossal,Jalal M. Fadili IEEE Transactions on Information Theory, 2013, 59 (4), pp.2001-2016. ⟨10.1109/TIT.2012.2233859⟩ Article dans une revue hal-00627452v5
Local Behavior of Sparse Analysis Regularization: Applications to Risk Estimation Samuel Vaiter,Charles Deledalle,Gabriel Peyré,Charles H Dossal,Jalal M. Fadili Applied and Computational Harmonic Analysis, 2013, 35 (3), pp.433-451. ⟨10.1016/j.acha.2012.11.006⟩ Article dans une revue hal-00687751v2

Communication dans un congrès (33)

Differentiable Generalized Sliced Wasserstein Plans Laetitia Chapel,Romain Tavenard,Samuel Vaiter NeurIPS 2025 - 39th Annual Conference on Neural Information Processing Systems, Dec 2025, San Diego, United States. pp.1-20, ⟨10.48550/arXiv.2505.22049⟩ Communication dans un congrès hal-05345011v1
Learning Theory for Kernel Bilevel Optimization Fares El Khoury,Edouard Pauwels,Samuel Vaiter,Michael Arbel NeurIPS 2025 - 39th Annual Conference on Neural Information Processing Systems, Dec 2025, San Diego, United States. pp.1-47 Communication dans un congrès hal-04950585v3
Provable local learning rule by expert aggregation for a Hawkes network Sophie Jaffard,Samuel Vaiter,Alexandre Muzy,Patricia Reynaud-Bouret AISTATS 2024 - The 27th International Conference on Artificial Intelligence and Statistics, May 2024, Valence, Spain Communication dans un congrès hal-04065229v2
A lower bound and a near-optimal algorithm for bilevel empirical risk minimization Mathieu Dagréou,Thomas Moreau,Samuel Vaiter,Pierre Ablin International Conference on Artificial Intelligence and Statistics (AISTATS), May 2024, Valencia, Spain. ⟨10.48550/arXiv.2302.08766⟩ Communication dans un congrès hal-04302861v3
Seeking universal approximation for continuous limits of graph neural networks on large random graphs Matthieu Cordonnier,Nicolas Keriven,Nicolas Tremblay,Samuel Vaiter Asilomar 2024 - Asilomar Conference on Signals, Systems, and Computers, Oct 2024, Pacific Grove, United States. pp.1-5 Communication dans un congrès hal-04831761v1
Derivatives of Stochastic Gradient Descent in parametric optimization Franck Iutzeler,Edouard Pauwels,S. Vaiter Advances in Neural Information Processing Systems (NeurIPS), Dec 2024, Vancouver, Canada, Canada. 24 p Communication dans un congrès hal-04582212v2
Seeking universal approximation for continuous counterparts of GNNs on large random graphs Matthieu Cordonnier,Nicolas Keriven,Nicolas Tremblay,Samuel Vaiter GSP 2024 - 7th Graph Signal Processing Workshop, Jun 2024, Delft, Netherlands Communication dans un congrès hal-04728922v1
Convergence of Graph Neural Networks with generic aggregation functions on random graphs Matthieu Cordonnier,Nicolas Keriven,Nicolas Tremblay,Samuel Vaiter GRETSI 2023 - XXIXème Colloque Francophone de Traitement du Signal et des Images, GRETSI - Groupe de Recherche en Traitement du Signal et des Images, Aug 2023, Grenoble, France. pp.1-4 Communication dans un congrès hal-04373554v1
Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Random Graphs Matthieu Cordonnier,Nicolas Keriven,Nicolas Tremblay,Samuel Vaiter GSP 2023 - 6th Graph Signal Processing workshop, Jun 2023, Oxford, United Kingdom. pp.1-3 Communication dans un congrès hal-04106511v1
One-step differentiation of iterative algorithms Jérôme Bolte,Edouard Pauwels,Samuel Vaiter Advances in Neural Information Processing Systems, 2023, New Orleans, United States Communication dans un congrès hal-04104382v1
On the Robustness of Text Vectorizers Rémi Catellier,Samuel Vaiter,Damien Garreau ICML 2023 - Fortieth International Conference on Machine Learning, Jul 2023, Honolulu, United States Communication dans un congrès hal-04403681v1
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding Nicolas Keriven,Samuel Vaiter NeurIPS 2023 - 37th Annual Conference on Neural Information Processing Systems, Dec 2023, New-Orleans, United States. pp.1-28 Communication dans un congrès hal-04103771v1
Automatic differentiation of nonsmooth iterative algorithms Jérôme Bolte,Edouard Pauwels,Samuel Vaiter Advances in Neural Information Processing Systems, Nov 2022, New Orleans, United States. 28 p Communication dans un congrès hal-03681143v1
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms Mathieu Dagréou,Pierre Ablin,Samuel Vaiter,Thomas Moreau Advances in Neural Information Processing Systems (NeurIPS), Nov 2022, New Orleans, United States Communication dans un congrès hal-03562151v2
Benchopt: Reproducible, efficient and collaborative optimization benchmarks Thomas Moreau,Mathurin Massias,Alexandre Gramfort,Pierre Ablin,Pierre-Antoine Bannieret al. NeurIPS 2022 - 36. Conference on Neural Information Processing Systems, Nov 2022, La Nouvelle Orléans, United States Communication dans un congrès hal-03830604v1
On the Universality of Graph Neural Networks on Large Random Graphs Nicolas Keriven,Alberto Bietti,Samuel Vaiter NeurIPS 2021 - 35th Conference on Neural Information Processing Systems, Dec 2021, Virtual, Canada Communication dans un congrès hal-03382553v1
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs Nicolas Keriven,Alberto Bietti,Samuel Vaiter NeurIPS 2020 - 34th Conference on Neural Information Processing Systems, Dec 2020, Vancouver (virtual), Canada Communication dans un congrès hal-02976711v1
Implicit differentiation of Lasso-type models for hyperparameter optimization Quentin Bertrand,Quentin Klopfenstein,Mathieu Blondel,Samuel Vaiter,Alexandre Gramfortet al. ICML 2020 - 37th International Conference on Machine Learning, Jul 2020, Vienna / Virtuel, Austria Communication dans un congrès hal-02532683v2
Refitting solutions promoted by $\ell_{12}$ sparse analysis regularization with block penalties Charles-Alban Deledalle,Nicolas Papadakis,Joseph Salmon,Samuel Vaiter International Conference on Scale Space and Variational Methods in Computer Vision (SSVM'19), Jun 2019, Hofgeismar, Germany. pp.131-143, ⟨10.1007/978-3-030-22368-7_11⟩ Communication dans un congrès hal-02059006v1
Exploiting regularity in sparse Generalized Linear Models Mathurin Massias,Samuel Vaiter,Alexandre Gramfort,Joseph Salmon SPARS 2019 - Signal Processing with Adaptive Sparse Structured Representations, Jul 2019, Toulouse, France Communication dans un congrès hal-02288859v1
Is the 1-norm the best convex sparse regularization? Yann Traonmilin,Samuel Vaiter,Rémi Gribonval iTWIST'18 - international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Nov 2018, Marseille, France. pp.1-11 Communication dans un congrès hal-01819219v1
Optimality of 1-norm regularization among weighted 1-norms for sparse recovery: a case study on how to find optimal regularizations Yann Traonmilin,Samuel Vaiter 8th International Conference on New Computational Methods for Inverse Problems, May 2018, Paris, France. pp.conference 1, ⟨10.1088/1742-6596/1131/1/012009⟩ Communication dans un congrès hal-01720871v3
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence Charles-Alban Deledalle,Nicolas Papadakis,Joseph Salmon,Samuel Vaiter Signal Processing with Adaptive Sparse Structured Representations (SPARS'17), Jun 2017, Lisbon, Portugal Communication dans un congrès hal-01412059v1
Stable Recovery with Analysis Decomposable Priors Jalal M. Fadili,Gabriel Peyré,Samuel Vaiter,Charles-Alban Deledalle,Joseph Salmon Proc. SampTA'13, Jul 2013, Bremen, Germany. pp.113-116 Communication dans un congrès hal-00926732v1
Robustesse au bruit des régularisations polyhédrales Samuel Vaiter,Gabriel Peyré,Jalal M. Fadili 24th GRETSI Symposium on Signal and Image Processing, Sep 2013, Brest, France. pp.ID130 Communication dans un congrès hal-00927075v1
Stable Recovery with Analysis Decomposable Priors Jalal M. Fadili,Gabriel Peyré,Samuel Vaiter,Charles-Alban Deledalle,Joseph Salmon SPARS 2013, Jul 2013, Lausanne, Switzerland. 1 pp Communication dans un congrès hal-00926727v1
The degrees of freedom of the group Lasso for a general design Samuel Vaiter,Gabriel Peyré,Jalal M. Fadili,Charles-Alban Deledalle,Charles H Dossal SPARS'13, Jul 2013, Lausanne, Switzerland. 1 page Communication dans un congrès hal-00926929v1
Reconstruction Stable par Régularisation Décomposable Analyse Jalal M. Fadili,Gabriel Peyré,Samuel Vaiter,Charles-Alban Deledalle,Joseph Salmon Colloque sur le Traitement du Signal et des Images (GRETSI'13), Sep 2013, Brest, France. pp.ID208 Communication dans un congrès hal-00927561v1
Robust Polyhedral Regularization Samuel Vaiter,Gabriel Peyré,Jalal M. Fadili International Conference on Sampling Theory and Applications (SampTA), 2013, Bremen, Germany Communication dans un congrès hal-00816377v1
Unbiased Risk Estimation for Sparse Analysis Regularization Charles Deledalle,Samuel Vaiter,Gabriel Peyré,Jalal M. Fadili,Charles H Dossal Proc. ICIP'12, Sep 2012, Orlando, United States. pp.3053-3056 Communication dans un congrès hal-00662718v1
Proximal Splitting Derivatives for Risk Estimation Charles Deledalle,Samuel Vaiter,Gabriel Peyré,Jalal M. Fadili,Charles H Dossal NCMIP'12, Apr 2012, France. pp.012003, ⟨10.1088/1742-6596/386/1/012003⟩ Communication dans un congrès hal-00670213v1
The Degrees of Freedom of the Group Lasso Samuel Vaiter,Charles Deledalle,Gabriel Peyré,Jalal M. Fadili,Charles H Dossal International Conference on Machine Learning Workshop (ICML), 2012, Edinburgh, United Kingdom Communication dans un congrès hal-00695292v1
Risk estimation for matrix recovery with spectral regularization Charles-Alban Deledalle,Samuel Vaiter,Gabriel Peyré,Jalal M. Fadili,Charles H Dossal ICML'2012 workshop on Sparsity, Dictionaries and Projections in Machine Learning and Signal Processing, Jun 2012, Edinburgh, United Kingdom Communication dans un congrès hal-00695326v3

Chapitre d'ouvrage (2)

Refitting Solutions Promoted by $$\ell _{12}$$ Sparse Analysis Regularizations with Block Penalties Charles-Alban Deledalle,Nicolas Papadakis,Joseph Salmon,Samuel Vaiter Scale Space and Variational Methods in Computer Vision, 11603, , pp.131-143, 2019, 978-3-030-22367-0. ⟨10.1007/978-3-030-22368-7_11⟩ Chapitre d'ouvrage hal-03107463v1
Low Complexity Regularization of Linear Inverse Problems Samuel Vaiter,Gabriel Peyré,Jalal M. Fadili Sampling Theory, a Renaissance, Pfander, Götz E. (Ed.), 50 p., 2015, 978-3-319-19748-7. ⟨10.1007/978-3-319-19749-4⟩ Chapitre d'ouvrage hal-01018927v3

Pré-publication, Document de travail (6)

Characterizations of inexact proximal operators Guillaume Lauga,Samuel Vaiter 2026 Pré-publication, Document de travail hal-05449026v1
Deep Equilibrium models for Poisson imaging inverse problems via Mirror Descent Christian Daniele,Silvia Villa,Samuel Vaiter,Luca Calatroni 2025 Pré-publication, Document de travail hal-05163926v1
CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration Sophie Jaffard,Samuel Vaiter,Patricia Reynaud-Bouret 2024 Pré-publication, Document de travail hal-04589554v2
Model identification and local linear convergence of coordinate descent Quentin Klopfenstein,Quentin Bertrand,Alexandre Gramfort,Joseph Salmon,Samuel Vaiter 2020 Pré-publication, Document de travail hal-03019711v1
Linear Support Vector Regression with Linear Constraints Quentin Klopfenstein,Samuel Vaiter 2019 Pré-publication, Document de travail hal-02349160v1
The degrees of freedom of the Group Lasso for a General Design Samuel Vaiter,Charles Deledalle,Gabriel Peyré,Jalal M. Fadili,Charles H Dossal 2012 Pré-publication, Document de travail hal-00768896v2

Rapport (1)

Model Consistency of Partly Smooth Regularizers Samuel Vaiter,Gabriel Peyré,Jalal M. Fadili [Research Report] CNRS. 2014 Rapport (rapport de recherche) hal-00987293v4

Thèse (2)

Low Complexity Regularization of Inverse Problems Samuel Vaiter General Mathematics [math.GM]. Université Paris Dauphine - Paris IX, 2014. English. ⟨NNT : 2014PA090055⟩ Thèse tel-01130672v1
Low Complexity Regularizations of Inverse Problems Samuel Vaiter Information Theory [math.IT]. Université Paris Dauphine - Paris IX, 2014. English. ⟨NNT : ⟩ Thèse tel-01026398v1

HDR (1)

From optimization to algorithmic differentiation: a graph detour Samuel Vaiter Optimization and Control [math.OC]. Université de Bourgogne, 2021 HDR tel-03159975v1