CEREMADE
Duval Vincent
Directeur de recherche INRIA
Biographie
Vincent Duval est chercheur INRIA dans l'équipe MOKAPLAN. Après des études d'ingénieur en mathématiques appliquées à l'Ecole polytechnique et Télécom ParisTech, il a obtenu son doctorat en 2011 à Télécom ParisTech pour une thèse sur les méthodes de débruitage en imagerie. Puis il a travaillé près de deux ans à l'ANSSI en tant qu'ingénieur. En 2013, il a rejoint l'équipe de Gabriel Peyré pour un postdoctorat au CEREMADE. En 2014, il a obtenu un poste de chercheur à INRIA (détaché du corps des Mines) dans l'équipe MOKAPLAN. Il a obtenu l'habilitation à diriger des recherches en 2022. Ses centres d'intérêt incluent les problèmes variationnels dans l'espace des mesures, avec des applications en traitement d'image ou en physique.
Publications
Articles
Duval V., Tovey R. (2024), Dynamical Programming for off-the-grid dynamic Inverse Problems, Control, Optimisation and Calculus of Variations, vol. 30, n°7 
De Castro Y., Duval V., Petit R. (2022), Towards Off-the-Grid Algorithms for Total Variation Regularized Inverse Problems, Journal of Mathematical Imaging and Vision, p. 25
Duval V. (2021), An Epigraphical Approach to the Representer Theorem, Journal of Convex Analysis, vol. 28, n°3, p. 819-836
Courbot J-B., Duval V., Legras B. (2020), Sparse analysis for mesoscale convective systems tracking, Signal Processing: Image Communication, vol. 85, p. 115854
Boyer C., Chambolle A., De Castro Y., Duval V., de Gournay F., Weiss P. (2019), On Representer Theorems and Convex Regularization, SIAM Journal on Optimization, vol. 29, n°2, p. 1260–1281
Denoyelle Q., Duval V., Peyré G., Soubies E. (2019), The Sliding Frank-Wolfe Algorithm and its Application to Super-Resolution Microscopy, Inverse Problems, vol. 36, n°1, p. 014001
Catala P., Duval V., Peyré G. (2019), A Low-Rank Approach to Off-the-Grid Sparse Superresolution, SIAM Journal on Imaging Sciences, vol. 12, n°3, p. 1464-1500
Duval V. (2019), A characterization of the Non-Degenerate Source Condition in super-resolution, Information and Inference, p. 1-31
Duval V., Benamou J-D. (2018), Minimal convex extensions and finite difference discretisation of the quadratic Monge–Kantorovich problem, European Journal of Applied Mathematics, p. 1-38
Catala P., Duval V., Peyré G. (2017), A Low-Rank Approach to Off-The-Grid Sparse Deconvolution, Journal of Physics. Conference Series, vol. 904, n°conférence 1
Dossal C., Duval V., Poon C. (2017), Sampling the Fourier transform along radial lines, SIAM Journal on Numerical Analysis, vol. 55, n°6, p. 2540-2564
Carlier G., Duval V., Peyré G., Schmitzer B. (2017), Convergence of Entropic Schemes for Optimal Transport and Gradient Flows, SIAM Journal on Mathematical Analysis, vol. 49, n°2, p. 1385-1418
Duval V., Peyré G. (2017), Sparse Spikes Super-resolution on Thin Grids II: the Continuous Basis Pursuit, Inverse Problems, vol. 33, n°9
Duval V., Peyré G. (2017), Sparse Regularization on Thin Grids I: the LASSO, Inverse Problems, vol. 33, n°5
Denoyelle Q., Duval V., Peyré G. (2016), Support Recovery for Sparse Super-Resolution of Positive Measures, Journal of Fourier Analysis and Applications, vol. 23, n°5, p. 1153–1194
Chambolle A., Duval V., Peyré G., Poon C. (2016), Geometric properties of solutions to the total variation denoising problem, Inverse Problems, vol. 33, n°1
Bleyer J., Carlier G., Duval V., Mirebeau J-M., Peyré G. (2016), A Γ-Convergence Result for the Upper Bound Limit Analysis of Plates, ESAIM: Mathematical Modelling and Numerical Analysis, vol. 50, n°1, p. 215-235
Duval V., Peyré G. (2015), Exact Support Recovery for Sparse Spikes Deconvolution, Foundations of Computational Mathematics, vol. 15, n°5, p. 1315-1355
Duval V., Peyré G. (2014), Low noise regimes for ℓ regularization : continuous and discrete settings, Proceedings in Applied Mathematics and Mechanics, vol. 14, n°1, p. 943–944
Communications avec actes
Chambolle A., Duval V., Machado J. (2023), The Total Variation-Wasserstein Problem, in Nielsen, F., Barbaresco, F. (eds), Berlin Heidelberg, Springer International Publishing, 610-619 p.
De Castro Y., Duval V., Petit R. (2021), Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems, in Abderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon, Scale Space and Variational Methods in Computer Vision, Proceedings of SSVM 2021, 553-564 p.
Denoyelle Q., Duval V., Peyré G., Soubies E. (2019), The Sliding Frank-Wolfe Algorithm for the BLASSO, in , Toulouse, Proceedings of the Workshop on Signal Processing with Adaptative Sparse Structured Representations -, 2 p.
Chambolle A., Duval V., Peyré G., Poon C. (2016), Total Variation Denoising and Support Localization of the Gradient, in , Cachan, Journal of Physics. Conference Series
Denoyelle Q., Duval V., Peyré G. (2015), Asymptotic of Sparse Support Recovery for Positive Measures, in , Journal of physics: Conference series
Duval V., Peyré G. (2015), The Non Degenerate Source Condition: Support Robustness for Discrete and Continuous Sparse Deconvolution, in , IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Dec 2015, Cancun, Mexico, Cancun, IEEE - Institute of Electrical and Electronics Engineers
Prépublications / Cahiers de recherche
Chambolle A., Duval V., Machado J. (2023), 1D approximation of measures in Wasserstein spaces, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 38 p.
Castro Y., Duval V., Petit R. (2023), Exact recovery of the support of piecewise constant images via total variation regularization, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 43 p.
Tovey R., Duval V. (2022), Dynamical Programming for off-the-grid dynamic Inverse Problems, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 43 p.
Duval V., Peyré G. (2015), Sparse Spikes Deconvolution on Thin Grids, Paris, Université Paris-Dauphine, 56 p.
Rapports
Duval V. (2014), A comparative analysis of the TVL1 and the TV-G models, Université Paris-Dauphine, 26 p.