Publications

See my Google Scholar profile for bibliometric data. Download the BibTeX file here.

2026¶

• Fairness-informed Pareto Optimization: An Efficient Bilevel Framework.
  S. Tanji, S. Vaiter, and Y. Laguel. Preprint. arXiv

2025¶

• Deep Equilibrium models for Poisson Imaging Inverse problems via Mirror Descent.
  C. Daniele, S. Villa, S. Vaiter, and L. Calatroni. (accepted to) SIAM Journal on Imaging Sciences. arXiv
• Geometric and computational hardness of bilevel programming.
  J. Bolte, T. Lê, E. Pauwels, and S. Vaiter. Mathematical Programming. DOI arXiv
• Risk Estimate under a Nonstationary Autoregressive Model for Data-Driven Reproduction Number Estimation.
  B. Pascal, and S. Vaiter. Signal Processing. DOI arXiv
• Differentiable Generalized Sliced Wasserstein Plans.
  L. Chapel, R. Tavenard, and S. Vaiter. Advances in Neural Information Processing Systems (NeurIPS). arXiv
• From Shortcut to Induction Head: How Data Diversity Shapes Algorithm Selection in Transformers.
  R. Kawata, Y. Song, A. Bietti, N. Nishikawa, T. Suzuki, S. Vaiter, and D. Wu. Advances in Neural Information Processing Systems (NeurIPS). arXiv
• Learning Theory for Kernel Bilevel Optimization.
  F. E. Khoury, E. Pauwels, S. Vaiter, and M. Arbel. Advances in Neural Information Processing Systems (NeurIPS). arXiv
• Bilevel gradient methods and Morse parametric qualification.
  J. Bolte, T. Lê, E. Pauwels, and S. Vaiter. Preprint. arXiv
• Faster Computation of Entropic Optimal Transport via Stable Low Frequency Modes.
  R. Chhaibi, S. Gratton, and S. Vaiter. Preprint. arXiv

2024¶

• A theory of optimal convex regularization for low-dimensional recovery.
  Y. Traonmilin, R. Gribonval, and S. Vaiter. Information and Inference: A Journal of the IMA. arXiv
• Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs.
  M. Cordonnier, N. Keriven, N. Tremblay, and S. Vaiter. Journal of Machine Learning Research. arXiv
• Model identification and local linear convergence of coordinate descent.
  Q. Klopfenstein, Q. Bertrand, A. Gramfort, J. Salmon, and S. Vaiter. Optimization Letters. arXiv
• Gradient Scarcity with Bilevel Optimization for Graph Learning.
  H. Ghanem, S. Vaiter, and N. Keriven. Transactions on Machine Learning Research. arXiv
• Derivatives of Stochastic Gradient Descent in parametric optimization.
  F. Iutzeler, E. Pauwels, and S. Vaiter. Advances in Neural Information Processing Systems (NeurIPS). arXiv
• A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization.
  M. Dagréou, T. Moreau, S. Vaiter, and P. Ablin. International Conference on Artificial Intelligence and Statistics (AISTATS). PDF arXiv
• Provable local learning rule by expert aggregation for a Hawkes network.
  S. Jaffard, S. Vaiter, A. Muzy, and P. Reynaud-Bouret. International Conference on Artificial Intelligence and Statistics (AISTATS). PDF arXiv
• How to compute Hessian-vector products?.
  M. Dagréou, P. Ablin, S. Vaiter, and T. Moreau. International Conference on Learning Representations (ICLR) Blogposts Track.
• CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration.
  S. Jaffard, S. Vaiter, and P. Reynaud-Bouret. Preprint. arXiv

2023¶

• Supervised learning of analysis-sparsity priors with automatic differentiation.
  H. Ghanem, J. Salmon, N. Keriven, and S. Vaiter. IEEE Signal Processing Letters. DOI arXiv
• The Derivatives of Sinkhorn-Knopp Converge.
  E. Pauwels, and S. Vaiter. SIAM Journal on Optimization. DOI PDF arXiv
• The Geometry of Sparse Analysis Regularization.
  X. Dupuis, and S. Vaiter. SIAM Journal on Optimization. DOI PDF arXiv
• One-step differentiation of iterative algorithms.
  J. Bolte, E. Pauwels, and S. Vaiter. Advances in Neural Information Processing Systems (NeurIPS). PDF arXiv
• What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding.
  N. Keriven, and S. Vaiter. Advances in Neural Information Processing Systems (NeurIPS). PDF arXiv
• Borne inférieure de compléxité et algorithme quasi-optimal pour la minimisation de risque empirique bi-niveaux.
  M. Dagréou, T. Moreau, S. Vaiter, and P. Ablin. Colloque Francophone de Traitement du Signal et des Images.
• Convergence of Message Passing Graph Neural Networks with Generic Aggregation on Random Graphs.
  M. Cordonnier, N. Keriven, N. Tremblay, and S. Vaiter. Colloque Francophone de Traitement du Signal et des Images.
• Convergence of Message Passing Graph Neural Networks with Generic Aggregation on Random Graphs.
  M. Cordonnier, N. Keriven, N. Tremblay, and S. Vaiter. Graph Signal Processing workshop.
• On the Robustness of Text Vectorizers.
  R. Catellier, S. Vaiter, and D. Garreau. International Conference on Machine Learning (ICML). arXiv

2022¶

• Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model.
  N. Keriven, and S. Vaiter. Electronic Journal of Statistics. DOI PDF arXiv
• Implicit differentiation for fast hyperparameter selection in non-smooth convex learning.
  Q. Bertrand, Q. Klopfenstein, M. Massias, M. Blondel, S. Vaiter, A. Gramfort, and J. Salmon. Journal of Machine Learning Research. PDF arXiv
• A framework for bilevel optimization that enables stochastic and global variance reduction algorithms.
  M. Dagréou, P. Ablin, S. Vaiter, and T. Moreau. Advances in Neural Information Processing Systems (NeurIPS). arXiv
• Automatic differentiation of nonsmooth iterative algorithms.
  J. Bolte, E. Pauwels, and S. Vaiter. Advances in Neural Information Processing Systems (NeurIPS). PDF arXiv
• Benchopt: Reproducible, efficient and collaborative optimization benchmarks.
  T. Moreau, M. Massias, A. Gramfort, P. Ablin, P. Bannier, B. Charlier, M. Dagréou, T. D. la Tour, G. Durif, C. F. Dantas, Q. Klopfenstein, J. Larsson, E. Lai, T. Lefort, M. Malezieu, B. Moufad, B. T. Nguyen, A. Rakotomamonjy, Z. Ramzie, J. Salmon, and S. Vaiter. Advances in Neural Information Processing Systems (NeurIPS). PDF arXiv
• Algorithmes stochastiques et réduction de variance grâce à un nouveau cadre pour l’optimisation bi-niveaux.
  M. Dagr{\'e}ou, P. Ablin, S. Vaiter, and T. Moreau. Colloque Francophone de Traitement du Signal et des Images.

2021¶

• Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation.
  B. Pascal, S. Vaiter, N. Pustelnik, and P. Abry. Journal of Mathematical Imaging and Vision. PDF arXiv
• Block based refitting in \(\ell_{12}\) sparse regularisation.
  C. Deledalle, N. Papadakis, J. Salmon, and S. Vaiter. Journal of Mathematical Imaging and Vision. DOI PDF arXiv
• Linear Support Vector Regression with Linear Constraints.
  Q. Klopfenstein, and S. Vaiter. Machine Learning. DOI arXiv
• On the Universality of Graph Neural Networks on Large Random Graphs.
  N. Keriven, A. Bietti, and S. Vaiter. Advances in Neural Information Processing Systems (NeurIPS). arXiv
• From optimization to algorithmic differentiation: a graph detour.
  S. Vaiter. PhD Thesis. PDF

2020¶

• Dual Extrapolation for Sparse Generalized Linear Models.
  M. Massias, S. Vaiter, A. Gramfort, and J. Salmon. Journal of Machine Learning Research. PDF arXiv
• Convergence and Stability of Graph Convolutional Networks on Large Random Graphs.
  N. Keriven, A. Bietti, and S. Vaiter. Advances in Neural Information Processing Systems (NeurIPS). PDF arXiv
• Implicit differentiation of Lasso-type models for hyperparameter optimization.
  Q. Bertrand, Q. Klopfenstein, M. Blondel, S. Vaiter, A. Gramfort, and J. Salmon. International Conference on Machine Learning (ICML). PDF arXiv

2019¶

• Maximal Solutions of Sparse Analysis Regularization.
  A. Barbara, A. Jourani, and S. Vaiter. Journal of Optimization Theory and Applications. DOI PDF arXiv
• Refitting solutions promoted by \(\ell_{12}\) sparse analysis regularization with block penalties.
  C. Deledalle, N. Papadakis, J. Salmon, and S. Vaiter. International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). arXiv
• Exploiting regularity in sparse Generalized Linear Models.
  M. Massias, S. Vaiter, A. Gramfort, and J. Salmon. Signal Processing with Adaptive Sparse Structured Representations (SPARS).

2018¶

• Model Consistency of Partly Smooth Regularizers.
  S. Vaiter, G. Peyré, and J. Fadili. IEEE Transactions on Information Theory. DOI PDF arXiv
• Optimality of 1-norm regularization among weighted 1-norms for sparse recovery: a case study on how to find optimal regularizations.
  Y. Traonmilin, and S. Vaiter. New Computational Methods for Inverse Problems (NCMIP). PDF arXiv
• Is the 1-norm the best convex sparse regularization?.
  Y. Traonmilin, S. Vaiter, and R. Gribonval. iTWIST. PDF arXiv

2017¶

• The Degrees of Freedom of Partly Smooth Regularizers.
  S. Vaiter, C. Deledalle, G. Peyré, J. Fadili, and C. Dossal. Annals of the Institute of Statistical Mathematics. DOI PDF arXiv
• A Sharp Oracle Inequality for Graph-Slope.
  P. Bellec, J. Salmon, and S. Vaiter. Electronic Journal of Statistics. DOI PDF arXiv
• Accelerated Alternating Descent Methods for Dykstra-like problems.
  A. Chambolle, P. Tan, and S. Vaiter. Journal of Mathematical Imaging and Vision. DOI PDF
• CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration.
  C. Deledalle, N. Papadakis, J. Salmon, and S. Vaiter. SIAM Journal on Imaging Sciences. DOI PDF arXiv
• Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence.
  C. Deledalle, N. Papadakis, J. Salmon, and S. Vaiter. Signal Processing with Adaptive Sparse Structured Representations (SPARS). PDF arXiv

2015¶

• Model Selection with Low Complexity Priors.
  S. Vaiter, M. Golbabaee, J. Fadili, and G. Peyré. Information and Inference: A Journal of the IMA. DOI PDF arXiv
• Low Complexity Regularization of Linear Inverse Problems.
  S. Vaiter, G. Peyré, and J. Fadili. DOI PDF arXiv

2014¶

• Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection.
  C. Deledalle, S. Vaiter, J. Fadili, and G. Peyré. SIAM Journal on Imaging Sciences. DOI PDF arXiv
• Low Complexity Regularizations of Inverse Problems.
  S. Vaiter. PhD Thesis. PDF

2013¶

• Local Behavior of Sparse Analysis Regularization: Applications to Risk Estimation.
  S. Vaiter, C. Deledalle, G. Peyré, C. Dossal, and J. Fadili. Applied and Computational Harmonic Analysis. DOI PDF arXiv
• Robust Sparse Analysis Regularization.
  S. Vaiter, G. Peyré, C. Dossal, and J. Fadili. IEEE Transactions on Information Theory. DOI PDF arXiv
• Reconstruction Stable par Régularisation Décomposable Analyse.
  J. Fadili, G. Peyré, S. Vaiter, C. Deledalle, and J. Salmon. Colloque Francophone de Traitement du Signal et des Images.
• Robustesse au bruit des régularisations polyhédrales.
  S. Vaiter, G. Peyré, and J. Fadili. Colloque Francophone de Traitement du Signal et des Images.
• Robust Polyhedral Regularization.
  S. Vaiter, G. Peyré, and J. Fadili. SAMPTA. PDF arXiv
• Stable Recovery with Analysis Decomposable Priors.
  J. Fadili, G. Peyré, S. Vaiter, C. Deledalle, and J. Salmon. SAMPTA. PDF arXiv
• The degrees of freedom of the group Lasso for a general design.
  S. Vaiter, G. Peyré, J. Fadili, C. Deledalle, and C. Dossal. Signal Processing with Adaptive Sparse Structured Representations (SPARS). PDF

2012¶

• Unbiased Risk Estimation for Sparse Analysis Regularization.
  C. Deledalle, S. Vaiter, G. Peyré, J. Fadili, and C. Dossal. International Conference on Image Processing (ICIP). DOI PDF
• Risk estimation for matrix recovery with spectral regularization.
  C. Deledalle, S. Vaiter, G. Peyré, J. Fadili, and C. Dossal. International Conference on Machine Learning (ICML) Workshop Sparsity, Dictionaries and Projections in Machine Learning and Signal Processing. PDF arXiv
• The Degrees of Freedom of the Group Lasso.
  S. Vaiter, C. Deledalle, G. Peyré, J. Fadili, and C. Dossal. International Conference on Machine Learning (ICML) Workshop Sparsity, Dictionaries and Projections in Machine Learning and Signal Processing. PDF arXiv
• Proximal Splitting Derivatives for Risk Estimation.
  C. Deledalle, S. Vaiter, G. Peyré, J. Fadili, and C. Dossal. New Computational Methods for Inverse Problems (NCMIP). DOI PDF