Yohann de Castro

According to our database, Yohann de Castro authored at least 28 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Second Maximum of a Gaussian Random Field and Exact (t-)Spacing test.

Jean-Marc Azaïs, Federico Dalmao, Yohann de Castro

CoRR, 2024

2023

Neural Networks beyond explainability: Selective inference for sequence motifs.

Antoine Villié, Philippe Veber, Yohann de Castro, Laurent Jacob

Trans. Mach. Learn. Res., 2023

Exact recovery of the support of piecewise constant images via total variation regularization.

Yohann de Castro, Vincent Duval, Romain Petit

CoRR, 2023

2022

Three rates of convergence or separation via U-statistics in a dependent framework.

Quentin Duchemin, Yohann de Castro, Claire Lacour

J. Mach. Learn. Res., 2022

Random Geometric Graph: Some recent developments and perspectives.

Quentin Duchemin, Yohann de Castro

CoRR, 2022

2021

Dual optimal design and the Christoffel-Darboux polynomial.

Yohann de Castro, Fabrice Gamboa, Didier Henrion, Jean-Bernard Lasserre

Optim. Lett., 2021

Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF).

Yohann de Castro, Luca Mencarelli

CoRR, 2021

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

Yohann de Castro, Vincent Duval, Romain Petit

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2021

2020

Markov Random Geometric Graph (MRGG): A Growth Model for Temporal Dynamic Networks.

Yohann de Castro, Quentin Duchemin

CoRR, 2020

2019

Nonnegative Matrix Factorization with Side Information for Time Series Recovery and Prediction.

Jiali Mei, Yohann de Castro, Yannig Goude, Jean-Marc Azaïs, Georges Hébrail

IEEE Trans. Knowl. Data Eng., 2019

On Representer Theorems and Convex Regularization.

Claire Boyer, Antonin Chambolle, Yohann de Castro, Vincent Duval, Frédéric de Gournay, Pierre Weiss

SIAM J. Optim., 2019

Sparse Regularization for Mixture Problems.

Yohann de Castro, Sébastien Gadat, Clément Marteau, Cathy Maugis-Rabusseau

CoRR, 2019

Multiple Testing and Variable Selection along Least Angle Regression's path.

Jean-Marc Azaïs, Yohann de Castro

CoRR, 2019

Latent distance estimation for random geometric graphs.

Ernesto Araya Valdivia, Yohann de Castro

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Convex Regularization and Representer Theorems.

Claire Boyer, Antonin Chambolle, Yohann de Castro, Vincent Duval, Frédéric de Gournay, Pierre Weiss

CoRR, 2018

2017

Exact Solutions to Super Resolution on Semi-Algebraic Domains in Higher Dimensions.

Yohann de Castro, Fabrice Gamboa, Didier Henrion, Jean B. Lasserre

IEEE Trans. Inf. Theory, 2017

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

Yohann de Castro, Elisabeth Gassiat, Sylvain Le Corff

IEEE Trans. Inf. Theory, 2017

Reconstructing Undirected Graphs from Eigenspaces.

Yohann de Castro, Thibault Espinasse, Paul Rochet

J. Mach. Learn. Res., 2017

Approximate Optimal Designs for Multivariate Polynomial Regression.

Yohann de Castro, Fabrice Gamboa, Didier Henrion, Roxana Hess, Jean-Bernard Lasserre

CoRR, 2017

Testing Gaussian Process with Applications to Super-Resolution.

Jean-Marc Azaïs, Yohann de Castro, Stéphane Mourareau

CoRR, 2017

Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates.

Jiali Mei, Yohann de Castro, Yannig Goude, Georges Hébrail

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Minimax Adaptive Estimation of Nonparametric Hidden Markov Models.

Yohann de Castro, Elisabeth Gassiat, Claire Lacour

J. Mach. Learn. Res., 2016

Restricted Isometry Constants for Gaussian and Rademacher matrices.

Sandrine Dallaporta, Yohann de Castro

CoRR, 2016

Adapting to unknown noise level in sparse deconvolution.

Claire Boyer, Yohann de Castro, Joseph Salmon

CoRR, 2016

(Non) convex losses and regularizations, some contributions in Statistics. (Pertes et régularisations (non) convexes, quelques contributions en Statistique).

Yohann de Castro

, 2016

2014

Optimal Designs for Lasso and Dantzig Selector Using Expander Codes.

Yohann de Castro

IEEE Trans. Inf. Theory, 2014

2011

A Remark on the Lasso and the Dantzig Selector

Yohann de Castro

CoRR, 2011

Exact Reconstruction using Support Pursuit

Yohann de Castro, Fabrice Gamboa

CoRR, 2011