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Samuel Vaiter

60
Documents

Publications

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
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Provable local learning rule by expert aggregation for a Hawkes network

Sophie Jaffard , Samuel Vaiter , Alexandre Muzy , Patricia Reynaud-Bouret
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS), inPress
Article dans une revue hal-04065229v2
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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
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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
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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
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Implicit differentiation for fast hyperparameter selection in non-smooth convex learning

Quentin Bertrand , Quentin Klopfenstein , Mathurin Massias , Mathieu Blondel , Samuel Vaiter
Journal of Machine Learning Research, 2022, 23 (149), pp.1-48
Article dans une revue hal-03228663v2

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
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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
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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

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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Benchopt: Reproducible, efficient and collaborative optimization benchmarks

Thomas Moreau , Mathurin Massias , Alexandre Gramfort , Pierre Ablin , Pierre-Antoine Bannier
NeurIPS 2022 - 36th Conference on Neural Information Processing Systems, Nov 2022, New Orleans, United States
Communication dans un congrès hal-03830604v1
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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

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
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Implicit differentiation of Lasso-type models for hyperparameter optimization

Quentin Bertrand , Quentin Klopfenstein , Mathieu Blondel , Samuel Vaiter , Alexandre Gramfort
ICML 2020 - 37th International Conference on Machine Learning, Jul 2020, Vienna / Virtuel, Austria
Communication dans un congrès hal-02532683v2

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
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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
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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
Communication dans un congrès hal-02059006v1
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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

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
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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
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Low Complexity Regularizations of Inverse Problems

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

Samuel Vaiter
General Mathematics [math.GM]. Université Paris Dauphine - Paris IX, 2014. English. ⟨NNT : 2014PA090055⟩
Thèse tel-01130672v1
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From optimization to algorithmic differentiation: a graph detour

Samuel Vaiter
Optimization and Control [math.OC]. Université de Bourgogne, 2021
HDR tel-03159975v1