Yohann de Castro

Orcid: 0000-0002-9008-7474

According to our database1, Yohann de Castro authored at least 27 papers between 2011 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Towards Off-the-Grid Algorithms for Total Variation Regularized Inverse Problems.
J. Math. Imaging Vis., January, 2023

Exact recovery of the support of piecewise constant images via total variation regularization.
CoRR, 2023

2022
Three rates of convergence or separation via U-statistics in a dependent framework.
J. Mach. Learn. Res., 2022

Neural Networks beyond explainability: Selective inference for sequence motifs.
CoRR, 2022

Random Geometric Graph: Some recent developments and perspectives.
CoRR, 2022

2021
Dual optimal design and the Christoffel-Darboux polynomial.
Optim. Lett., 2021

Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF).
CoRR, 2021

2020
Markov Random Geometric Graph (MRGG): A Growth Model for Temporal Dynamic Networks.
CoRR, 2020

2019
Nonnegative Matrix Factorization with Side Information for Time Series Recovery and Prediction.
IEEE Trans. Knowl. Data Eng., 2019

On Representer Theorems and Convex Regularization.
SIAM J. Optim., 2019

Sparse Regularization for Mixture Problems.
CoRR, 2019

Multiple Testing and Variable Selection along Least Angle Regression's path.
CoRR, 2019

Latent distance estimation for random geometric graphs.
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.
CoRR, 2018

2017
Exact Solutions to Super Resolution on Semi-Algebraic Domains in Higher Dimensions.
IEEE Trans. Inf. Theory, 2017

Consistent Estimation of the Filtering and Marginal Smoothing Distributions in Nonparametric Hidden Markov Models.
IEEE Trans. Inf. Theory, 2017

Reconstructing Undirected Graphs from Eigenspaces.
J. Mach. Learn. Res., 2017

Approximate Optimal Designs for Multivariate Polynomial Regression.
CoRR, 2017

Testing Gaussian Process with Applications to Super-Resolution.
CoRR, 2017

Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Minimax Adaptive Estimation of Nonparametric Hidden Markov Models.
J. Mach. Learn. Res., 2016

Restricted Isometry Constants for Gaussian and Rademacher matrices.
CoRR, 2016

Adapting to unknown noise level in sparse deconvolution.
CoRR, 2016

(Non) convex losses and regularizations, some contributions in Statistics. (Pertes et régularisations (non) convexes, quelques contributions en Statistique).
, 2016

2014
Optimal Designs for Lasso and Dantzig Selector Using Expander Codes.
IEEE Trans. Inf. Theory, 2014

2011
A Remark on the Lasso and the Dantzig Selector
CoRR, 2011

Exact Reconstruction using Support Pursuit
CoRR, 2011


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