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Number of documents

45

Samuel Vaiter's publications


Journal articles14 documents

  • Barbara Pascal, Samuel Vaiter, Nelly Pustelnik, Patrice Abry. Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation. Journal of Mathematical Imaging and Vision, Springer Verlag, 2021, 63, pp.923-952. ⟨10.1007/s10851-021-01035-1⟩. ⟨hal-03044181⟩
  • Quentin Klopfenstein, Samuel Vaiter. Linear support vector regression with linear constraints. Machine Learning, Springer Verlag, 2021, 110 (7), pp.1939-1974. ⟨10.1007/s10994-021-06018-2⟩. ⟨hal-03303248⟩
  • Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter. Block based refitting in $\ell_{12}$ sparse regularisation. Journal of Mathematical Imaging and Vision, Springer Verlag, 2021, pp.216-236. ⟨10.1007/s10851-020-00993-2⟩. ⟨hal-02330441⟩
  • Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon. Dual Extrapolation for Sparse Generalized Linear Models. Journal of Machine Learning Research, Microtome Publishing, 2020, 21 (234), pp.1-33. ⟨hal-02263500⟩
  • Abdessamad Barbara, Abderrahim Jourani, Samuel Vaiter. Maximal Solutions of Sparse Analysis Regularization. Journal of Optimization Theory and Applications, Springer Verlag, 2019, 180 (2), pp.374-396. ⟨10.1007/s10957-018-1385-3⟩. ⟨hal-01467965⟩
  • Samuel Vaiter, Gabriel Peyré, Jalal M. Fadili. Model Consistency of Partly Smooth Regularizers. IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2018, 64 (3), pp.1725-1737. ⟨10.1109/TIT.2017.2713822⟩. ⟨hal-01658847⟩
  • Antonin Chambolle, Pauline Tan, Samuel Vaiter. Accelerated Alternating Descent Methods for Dykstra-like problems. Journal of Mathematical Imaging and Vision, Springer Verlag, 2017, 59 (3), pp.481-497. ⟨10.1007/s10851-017-0724-6⟩. ⟨hal-01346532⟩
  • Samuel Vaiter, Charles-Alban Deledalle, Jalal M. Fadili, Gabriel Peyré, Charles H Dossal. The degrees of freedom of partly smooth regularizers . Annals of the Institute of Statistical Mathematics, Springer Verlag, 2017, 69 (4), pp.791 - 832. ⟨10.1007/s10463-016-0563-z⟩. ⟨hal-00981634v4⟩
  • Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter. CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration. SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2017, 10 (1), pp.243-284. ⟨10.1137/16M1080318⟩. ⟨hal-01333295v3⟩
  • Pierre C. Bellec, Joseph Salmon, Samuel Vaiter. A sharp oracle inequality for Graph-Slope. Electronic Journal of Statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2017, 11 (2), pp.4851-4870. ⟨10.1214/17-EJS1364⟩. ⟨hal-01544680⟩
  • Samuel Vaiter, Mohammad Golbabaee, Jalal M. Fadili, Gabriel Peyré. Model Selection with Low Complexity Priors. Information and Inference, Oxford University Press (OUP), 2015, 52 p. ⟨hal-00842603v3⟩
  • Charles-Alban Deledalle, Samuel Vaiter, Jalal M. Fadili, Gabriel Peyré. Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection. SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2014, 7 (4), pp.2448-2487. ⟨hal-00987295v2⟩
  • Samuel Vaiter, Charles Deledalle, Gabriel Peyré, Charles H Dossal, Jalal M. Fadili. Local Behavior of Sparse Analysis Regularization: Applications to Risk Estimation. Applied and Computational Harmonic Analysis, Elsevier, 2013, 35 (3), pp.433-451. ⟨10.1016/j.acha.2012.11.006⟩. ⟨hal-00687751v2⟩
  • Samuel Vaiter, Gabriel Peyré, Charles H Dossal, Jalal M. Fadili. Robust Sparse Analysis Regularization. IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2013, 59 (4), pp.2001-2016. ⟨10.1109/TIT.2012.2233859⟩. ⟨hal-00627452v5⟩

Conference papers17 documents

  • Nicolas Keriven, Alberto Bietti, Samuel Vaiter. Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. NeurIPS 2020 - 34th Conference on Neural Information Processing Systems, Dec 2020, Vancouver (virtual), Canada. ⟨hal-02976711⟩
  • Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, et al.. Implicit differentiation of Lasso-type models for hyperparameter optimization. ICML 2020 - 37th International Conference on Machine Learning, Jul 2020, Vienna / Virtuel, Austria. ⟨hal-02532683v2⟩
  • Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter. Refitting solutions promoted by $\ell_{12}$ sparse analysis regularization with block penalties. International Conference on Scale Space and Variational Methods in Computer Vision (SSVM'19), Jun 2019, Hofgeismar, Germany. pp.131-143. ⟨hal-02059006⟩
  • Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon. Exploiting regularity in sparse Generalized Linear Models. SPARS 2019 - Signal Processing with Adaptive Sparse Structured Representations, Jul 2019, Toulouse, France. ⟨hal-02288859⟩
  • Yann Traonmilin, Samuel Vaiter, Rémi Gribonval. Is the 1-norm the best convex sparse regularization?. iTWIST'18 - international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Nov 2018, Marseille, France. pp.1-11. ⟨hal-01819219⟩
  • Yann Traonmilin, Samuel Vaiter. Optimality of 1-norm regularization among weighted 1-norms for sparse recovery: a case study on how to find optimal regularizations. 8th International Conference on New Computational Methods for Inverse Problems, May 2018, Paris, France. pp.conference 1, ⟨10.1088/1742-6596/1131/1/012009⟩. ⟨hal-01720871v3⟩
  • Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter. Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence. Signal Processing with Adaptive Sparse Structured Representations (SPARS'17), Jun 2017, Lisbon, Portugal. ⟨hal-01412059⟩
  • Samuel Vaiter, Gabriel Peyré, Jalal M. Fadili. Robustesse au bruit des régularisations polyhédrales. 24th GRETSI Symposium on Signal and Image Processing, Sep 2013, Brest, France. pp.ID130. ⟨hal-00927075⟩
  • Jalal M. Fadili, Gabriel Peyré, Samuel Vaiter, Charles-Alban Deledalle, Joseph Salmon. Stable Recovery with Analysis Decomposable Priors. SPARS 2013, Jul 2013, Lausanne, Switzerland. 1 pp. ⟨hal-00926727⟩
  • Jalal M. Fadili, Gabriel Peyré, Samuel Vaiter, Charles-Alban Deledalle, Joseph Salmon. Stable Recovery with Analysis Decomposable Priors. Proc. SampTA'13, Jul 2013, Bremen, Germany. pp.113-116. ⟨hal-00926732⟩
  • Samuel Vaiter, Gabriel Peyré, Jalal M. Fadili, Charles-Alban Deledalle, Charles H Dossal. The degrees of freedom of the group Lasso for a general design. SPARS'13, Jul 2013, Lausanne, Switzerland. 1 page. ⟨hal-00926929⟩
  • Samuel Vaiter, Gabriel Peyré, Jalal M. Fadili. Robust Polyhedral Regularization. International Conference on Sampling Theory and Applications (SampTA), 2013, Bremen, Germany. ⟨hal-00816377⟩
  • Jalal M. Fadili, Gabriel Peyré, Samuel Vaiter, Charles-Alban Deledalle, Joseph Salmon. Reconstruction Stable par Régularisation Décomposable Analyse. Colloque sur le Traitement du Signal et des Images (GRETSI'13), Sep 2013, Brest, France. pp.ID208. ⟨hal-00927561⟩
  • Charles Deledalle, Samuel Vaiter, Gabriel Peyré, Jalal M. Fadili, Charles H Dossal. Proximal Splitting Derivatives for Risk Estimation. NCMIP'12, Apr 2012, France. pp.012003, ⟨10.1088/1742-6596/386/1/012003⟩. ⟨hal-00670213⟩
  • Charles Deledalle, Samuel Vaiter, Gabriel Peyré, Jalal M. Fadili, Charles H Dossal. Unbiased Risk Estimation for Sparse Analysis Regularization. Proc. ICIP'12, Sep 2012, Orlando, United States. pp.3053-3056. ⟨hal-00662718⟩
  • Samuel Vaiter, Charles Deledalle, Gabriel Peyré, Jalal M. Fadili, Charles H Dossal. The Degrees of Freedom of the Group Lasso. International Conference on Machine Learning Workshop (ICML), 2012, Edinburgh, United Kingdom. ⟨hal-00695292⟩
  • Charles-Alban Deledalle, Samuel Vaiter, Gabriel Peyré, Jalal M. Fadili, Charles H Dossal. Risk estimation for matrix recovery with spectral regularization. ICML'2012 workshop on Sparsity, Dictionaries and Projections in Machine Learning and Signal Processing, Jun 2012, Edinburgh, United Kingdom. ⟨hal-00695326v3⟩

Book sections2 documents

  • Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter. Refitting Solutions Promoted by $$\ell _{12}$$ Sparse Analysis Regularizations with Block Penalties. 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⟩. ⟨hal-03107463⟩
  • Samuel Vaiter, Gabriel Peyré, Jalal M. Fadili. Low Complexity Regularization of Linear Inverse Problems. Sampling Theory, a Renaissance, Pfander, Götz E. (Ed.), 50 p., 2015, 978-3-319-19748-7. ⟨10.1007/978-3-319-19749-4⟩. ⟨hal-01018927v3⟩

Preprints, Working Papers, ...9 documents

  • Edouard Pauwels, Samuel Vaiter. The derivatives of Sinkhorn-Knopp converge. 2022. ⟨hal-03736905v2⟩
  • Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau. A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. 2022. ⟨hal-03562151⟩
  • Xavier Dupuis, Samuel Vaiter. The Geometry of Sparse Analysis Regularization. 2022. ⟨hal-02169356v2⟩
  • Jérôme Bolte, Edouard Pauwels, Samuel Vaiter. Automatic differentiation of nonsmooth iterative algorithms. 2022. ⟨hal-03681143⟩
  • Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, et al.. Implicit differentiation for fast hyperparameter selection in non-smooth convex learning. 2021. ⟨hal-03228663⟩
  • Quentin Klopfenstein, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon, Samuel Vaiter. Model identification and local linear convergence of coordinate descent. 2020. ⟨hal-03019711⟩
  • Nicolas Keriven, Samuel Vaiter. Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model. 2020. ⟨hal-02484970⟩
  • Quentin Klopfenstein, Samuel Vaiter. Linear Support Vector Regression with Linear Constraints. 2019. ⟨hal-02349160⟩
  • Samuel Vaiter, Charles Deledalle, Gabriel Peyré, Jalal M. Fadili, Charles H Dossal. The degrees of freedom of the Group Lasso for a General Design. 2012. ⟨hal-00768896v2⟩

Reports1 document

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

Theses1 document

  • Samuel Vaiter. Low Complexity Regularizations of Inverse Problems. Information Theory [math.IT]. Université Paris Dauphine - Paris IX, 2014. English. ⟨tel-01026398⟩

Habilitation à diriger des recherches1 document

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