Samuel Vaiter

Documents

Publications

	Gradient scarcity with Bilevel Optimization for Graph Learning Hashem Ghanem , Samuel Vaiter , Nicolas Keriven Transactions on Machine Learning Research Journal, 2024 Journal articles 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⟩ Journal articles hal-04308828v1
	A theory of optimal convex regularization for low-dimensional recovery Yann Traonmilin , Rémi Gribonval , Samuel Vaiter Information and Inference, 2024, 13 (2) Journal articles hal-03467123v3
	The Geometry of Sparse Analysis Regularization Xavier Dupuis , Samuel Vaiter SIAM Journal on Optimization, 2023, 33 (2), pp.842-867. ⟨10.1137/19M1271877⟩ Journal articles hal-02169356v2
	The derivatives of Sinkhorn-Knopp converge Edouard Pauwels , Samuel Vaiter SIAM Journal on Optimization, 2023, 33 (3), ⟨10.48550/arXiv.2207.12717⟩ Journal articles 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⟩ Journal articles hal-03518852v1
	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⟩ Journal articles hal-02484970v1
	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 Journal articles hal-03228663v2
	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⟩ Journal articles 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⟩ Journal articles hal-03303248v1
	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⟩ Journal articles 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 Journal articles 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⟩ Journal articles 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⟩ Journal articles hal-01658847v1
	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⟩ Journal articles hal-01333295v3
	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⟩ Journal articles hal-00981634v4
	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⟩ Journal articles hal-01544680v1
	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⟩ Journal articles hal-01346532v1
	Model Selection with Low Complexity Priors Samuel Vaiter , Mohammad Golbabaee , Jalal M. Fadili , Gabriel Peyré Information and Inference, 2015, 52 p Journal articles 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 Journal articles hal-00987295v2
	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⟩ Journal articles hal-00687751v2
	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⟩ Journal articles hal-00627452v5

	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⟩ Conference papers hal-04302861v3
	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 Conference papers hal-04065229v2
	One-step differentiation of iterative algorithms Jérôme Bolte , Edouard Pauwels , Samuel Vaiter Advances in Neural Information Processing Systems, 2023, New Orleans, United States Conference papers 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 Conference papers hal-04403681v1
	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 Conference papers hal-04106511v1
	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 Conference papers hal-04103771v1
	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 Conference papers hal-04373554v1
	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 Conference papers hal-03681143v1
	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 Conference papers hal-03830604v1
	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 Conference papers 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 Conference papers hal-03382553v1
	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 Conference papers 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 Conference papers hal-02976711v1
	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 Conference papers hal-02288859v1
	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 Conference papers hal-02059006v1
	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 Conference papers 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⟩ Conference papers 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 Conference papers hal-01412059v1
	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 Conference papers hal-00926929v1
	Robust Polyhedral Regularization Samuel Vaiter , Gabriel Peyré , Jalal M. Fadili International Conference on Sampling Theory and Applications (SampTA), 2013, Bremen, Germany Conference papers hal-00816377v1
	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 Conference papers hal-00927561v1
	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 Conference papers hal-00926727v1
	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 Conference papers hal-00927075v1
	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 Conference papers hal-00926732v1
	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 Conference papers 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⟩ Conference papers hal-00670213v1
	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 Conference papers hal-00695326v3
	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 Conference papers hal-00695292v1

	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⟩ Book sections 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⟩ Book sections hal-01018927v3

	Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs Matthieu Cordonnier , Nicolas Keriven , Nicolas Tremblay , Samuel Vaiter 2024 Preprints, Working Papers, ... hal-04059402v3
	Derivatives of Stochastic Gradient Descent Franck Iutzeler , Edouard Pauwels , S. Vaiter 2024 Preprints, Working Papers, ... hal-04582212v1
	Geometric and computational hardness of bilevel programming Jérôme Bolte , Quoc-Tung Le , Edouard Pauwels , Samuel Vaiter 2024 Preprints, Working Papers, ... hal-04649435v1
	CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration Sophie Jaffard , Samuel Vaiter , Patricia Reynaud-Bouret 2024 Preprints, Working Papers, ... hal-04589554v1
	Model identification and local linear convergence of coordinate descent Quentin Klopfenstein , Quentin Bertrand , Alexandre Gramfort , Joseph Salmon , Samuel Vaiter 2020 Preprints, Working Papers, ... hal-03019711v1
	Linear Support Vector Regression with Linear Constraints Quentin Klopfenstein , Samuel Vaiter 2019 Preprints, Working Papers, ... 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 Preprints, Working Papers, ... hal-00768896v2

	Model Consistency of Partly Smooth Regularizers Samuel Vaiter , Gabriel Peyré , Jalal M. Fadili [Research Report] CNRS. 2014 Reports hal-00987293v4

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

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

Samuel Vaiter

Publications

Gradient scarcity with Bilevel Optimization for Graph Learning

Local linear convergence of proximal coordinate descent algorithm

A theory of optimal convex regularization for low-dimensional recovery

The Geometry of Sparse Analysis Regularization

The derivatives of Sinkhorn-Knopp converge

Supervised learning of analysis-sparsity priors with automatic differentiation

Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model

Implicit differentiation for fast hyperparameter selection in non-smooth convex learning

Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation

Linear support vector regression with linear constraints

Block based refitting in $\ell_{12}$ sparse regularisation

Dual Extrapolation for Sparse Generalized Linear Models

Maximal Solutions of Sparse Analysis Regularization

Model Consistency of Partly Smooth Regularizers

CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration

The degrees of freedom of partly smooth regularizers

A sharp oracle inequality for Graph-Slope

Accelerated Alternating Descent Methods for Dykstra-like problems

Model Selection with Low Complexity Priors

Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection

Local Behavior of Sparse Analysis Regularization: Applications to Risk Estimation

Robust Sparse Analysis Regularization

A lower bound and a near-optimal algorithm for bilevel empirical risk minimization

Provable local learning rule by expert aggregation for a Hawkes network

One-step differentiation of iterative algorithms

On the Robustness of Text Vectorizers

Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Random Graphs

What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding

Convergence of Graph Neural Networks with generic aggregation functions on random graphs

Automatic differentiation of nonsmooth iterative algorithms

Benchopt: Reproducible, efficient and collaborative optimization benchmarks

A framework for bilevel optimization that enables stochastic and global variance reduction algorithms

On the Universality of Graph Neural Networks on Large Random Graphs

Implicit differentiation of Lasso-type models for hyperparameter optimization

Convergence and Stability of Graph Convolutional Networks on Large Random Graphs

Exploiting regularity in sparse Generalized Linear Models

Refitting solutions promoted by $\ell_{12}$ sparse analysis regularization with block penalties

Is the 1-norm the best convex sparse regularization?

Optimality of 1-norm regularization among weighted 1-norms for sparse recovery: a case study on how to find optimal regularizations

Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence

The degrees of freedom of the group Lasso for a general design

Robust Polyhedral Regularization

Reconstruction Stable par Régularisation Décomposable Analyse

Stable Recovery with Analysis Decomposable Priors

Robustesse au bruit des régularisations polyhédrales

Stable Recovery with Analysis Decomposable Priors

Unbiased Risk Estimation for Sparse Analysis Regularization

Proximal Splitting Derivatives for Risk Estimation

Risk estimation for matrix recovery with spectral regularization

The Degrees of Freedom of the Group Lasso

Refitting Solutions Promoted by $$\ell _{12}$$ Sparse Analysis Regularizations with Block Penalties

Low Complexity Regularization of Linear Inverse Problems

Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs

Derivatives of Stochastic Gradient Descent

Geometric and computational hardness of bilevel programming

CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration

Model identification and local linear convergence of coordinate descent

Linear Support Vector Regression with Linear Constraints

The degrees of freedom of the Group Lasso for a General Design

Model Consistency of Partly Smooth Regularizers

Low Complexity Regularization of Inverse Problems

Low Complexity Regularizations of Inverse Problems

From optimization to algorithmic differentiation: a graph detour