Multiple testing and variable selection along the path of the least angle regression

Autor: Jean-Marc Azaïs, Yohann De Castro

Publikováno v: Information and Inference: A Journal of the IMA. 11:1329-1388

We investigate multiple testing and variable selection using the Least Angle Regression (LARS) algorithm in high dimensions under the assumption of Gaussian noise. LARS is known to produce a piecewise affine solution path with change points referred

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fed9887120f2a5a155464ac4fb076b87
https://doi.org/10.1093/imaiai/iaac018

Adaptive estimation of nonparametric geometric graphs

Autor: Yohann De Castro, Thanh Mai Pham Ngoc, Claire Lacour

Publikováno v: Mathematical Statistics and Learning
Mathematical Statistics and Learning, EMS Publishing House, 2019, ⟨10.4171/MSL/15⟩

This article studies the recovery of graphons when they are convolution kernels on compact (symmetric) metric spaces. This case is of particular interest since it covers the situation where the probability of an edge depends only on some unknown nonp

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a87722b9bdd4555b845e436b13b7f84c
https://doi.org/10.4171/msl/15

Testing Gaussian process with applications to super-resolution

Autor: Stéphane Mourareau, Yohann De Castro, Jean-Marc Azaïs

Publikováno v: Applied and Computational Harmonic Analysis. 48:445-481

This article introduces exact testing procedures on the mean of a Gaussian process $X$ derived from the outcomes of $\ell_1$-minimization over the space of complex valued measures. The process $X$ can thought as the sum of two terms: first, the convo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::07d6ef8354ba56a0b7325a5c3bf8f48d
https://doi.org/10.1016/j.acha.2018.07.001

Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems

Autor: Yohann de Castro, Vincent Duval, Romain Petit

Publikováno v: Scale Space and Variational Methods in Computer Vision
Elmoataz, Abderrahim; Fadili, Jalal; Quéau, Yvain; Rabin, Julien; Simon, Loïc. Scale Space and Variational Methods in Computer Vision, 12679, Springer, Cham, pp.553-564, 2021, Lecture Notes in Computer Sciences, ⟨10.1007/978-3-030-75549-2_44⟩
8th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2021)
8th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2021), May 2021, Online event, France. pp.553-564, ⟨10.1007/978-3-030-75549-2_44⟩
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision, Springer Verlag, 2022, ⟨10.1007/s10851-022-01115-w⟩
Journal of Mathematical Imaging and Vision, 2022, ⟨10.1007/s10851-022-01115-w⟩
HAL
Lecture Notes in Computer Science ISBN: 9783030755485
SSVM

We introduce an algorithm to solve linear inverse problems regularized with the total (gradient) variation in a gridless manner. Contrary to most existing methods, that produce an approximate solution which is piecewise constant on a fixed mesh, our

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d91b896db75f47bf7b2ab9926b623cbd
https://inria.hal.science/hal-03196916v2/document

Consistent Estimation of the Filtering and Marginal Smoothing Distributions in Nonparametric Hidden Markov Models

Autor: Yohann De Castro, Sylvain Le Corff, Elisabeth Gassiat

Publikováno v: HAL
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2017, 63 (8), pp.4758--47777

In this paper, we consider the filtering and smoothing recursions in nonparametric finite state space hidden Markov models (HMMs) when the parameters of the model are unknown and replaced by estimators. We provide an explicit and time uniform control

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::41f787e8ce5a9bb85ccfb9473f8be820
https://doi.org/10.1109/tit.2017.2696959