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Provable local learning rule by expert aggregation for a Hawkes network

Sophie Jaffard , Samuel Vaiter , Alexandre Muzy , Patricia Reynaud-Bouret
2023
Preprints, Working Papers, ... hal-04065229v1
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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⟩
Journal articles hal-01333295v3
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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⟩
Theses tel-01130672v1
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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⟩
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
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Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs

Matthieu Cordonnier , Nicolas Keriven , Nicolas Tremblay , Samuel Vaiter
2023
Preprints, Working Papers, ... hal-04059402v2
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Gradient scarcity with Bilevel Optimization for Graph Learning

Hashem Ghanem , Samuel Vaiter , Nicolas Keriven
2023
Preprints, Working Papers, ... hal-04041721v1
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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
Conference papers hal-02059006v1

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
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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, In press, 30 (339-343), ⟨10.1109/LSP.2023.3244511⟩
Journal articles hal-03518852v1
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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⟩
Journal articles hal-00687751v2
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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⟩
Journal articles hal-00627452v5
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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
Conference papers hal-00926732v1
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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
Journal articles hal-00842603v3
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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⟩
Journal articles hal-01346532v1
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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
Conference papers hal-01412059v1
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The derivatives of Sinkhorn-Knopp converge

Edouard Pauwels , Samuel Vaiter
2022
Preprints, Working Papers, ... hal-03736905v2
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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
Journal articles hal-00987295v2
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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 (GSP 2023), Jun 2023, Oxford, United Kingdom
Conference papers hal-04106511v1
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A theory of optimal convex regularization for low-dimensional recovery

Yann Traonmilin , Rémi Gribonval , Samuel Vaiter
2022
Preprints, Working Papers, ... hal-03467123v2
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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
Conference papers hal-01819219v1
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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⟩
Journal articles hal-00981634v4
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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⟩
Conference papers hal-00670213v1
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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
Conference papers hal-00662718v1
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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⟩
Journal articles hal-01467965v1
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Linear Support Vector Regression with Linear Constraints

Quentin Klopfenstein , Samuel Vaiter
2019
Preprints, Working Papers, ... hal-02349160v1
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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
Conference papers hal-02288859v1
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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
Conference papers hal-03681143v1
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The Geometry of Sparse Analysis Regularization

Xavier Dupuis , Samuel Vaiter
2022
Preprints, Working Papers, ... hal-02169356v2

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