Yohann De Castro

My complete list of publications is available on HAL (Open Archives).

• Gaussian random field's anisotropy using excursion sets.
  J.-M. Azaïs, Y. De Castro, F. Dalmao.
  Under review, 2025.
  Probability Geometry PDF Code
• Second Maximum of a Gaussian Random Field and Exact (t-)Spacing test.
  J.-M. Azaïs, Y. De Castro, F. Dalmao.
  Under review, 2025.
  Probability Geometry PDF Code
• Gaussian Mixture Model with unknown diagonal covariances via continuous sparse regularization.
  Y. De Castro, R. Giard, C. Marteau.
  Under review, 2025.
  Learning Optimization PDF Zenodo
• Effective regions and kernels in continuous sparse regularization.
  Y. De Castro, R. Gribonval, N. Jouvin.
  Under review, 2025.
  Learning Optimization PDF
• Node Regression on Latent Position Random Graphs via Local Averaging
  Martin Gjorgjevski, Nicolas Keriven, Simon Barthelmé, Yohann De Castro.
  Under review, 2025.
  Graph Optimization PDF
• Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems
  Pierre-Cyril Aubin-Frankowski, Yohann De Castro, Axel Parmentier, Alessandro Rudi.
  Under review, 2025.
  Learning Optimization PDF
• FastPart: Over-Parameterized Stochastic Gradient Descent for Sparse optimisation on Measures.
  Y. De Castro, S. Gadat, C. Marteau.
  ArXiv Preprint, 2023 (Revised 2025).
  Optimization Probability PDF ArXiv
• Exact recovery of the support of piecewise constant images via total variation regularization.
  Y. De Castro, V. Duval, R. Petit.
  Inverse Problems, 40(10), 2024.
  Inverse Problems PDF Code
• SIGLE: valid Selective Inference procedure for Generalized Linear Lasso.
  Y. De Castro, Q. Duchemin.
  Under review.
  Learning Probability PDF Code
• Neural Networks beyond explainability: Selective inference for sequence motifs.
  Y. De Castro, A. Villié, P. Veber, L. Jacob.
  Transactions on Machine Learning Research (TMLR), 2023.
  Learning Probability PDF OpenReview
• Random Geometric Graph: Some recent developments and perspectives.
  Q. Duchemin, Y. De Castro.
  High Dimensional Probability IX, pp 347–392, 2023.
  Graphs Probability PDF
• Minimax Estimation of Partially-Observed Vector AutoRegressions.
  Guillaume Dalle, Yohann De Castro.
  Electronic Journal of Statistics, 2025.
  Graphs Probability PDF Code
• Markov Random Geometric Graph (MRGG): A Growth Model for Temporal Dynamic Networks.
  Y. De Castro, Q. Duchemin.
  Electronic Journal of Statistics, 16, 671-699, 2022.
  Graphs Probability PDF Code
• Towards off-the-grid algorithms for total variation regularized inverse problems.
  Y. De Castro, R. Petit, V. Duval.
  Journal of Mathematical Imaging and Vision (JMIV), 2022.
  Inverse Problems PDF Code
• Multiple Testing and Variable Selection along Least Angle Regression's path.
  Y. De Castro, J-M Azaïs.
  Information & Inference, 2022.
  Learning Probability PDF Code
• Three rates of convergence or separation via U-statistics in a dependent framework.
  Y. De Castro, Q. Duchemin, C. Lacour.
  Journal of Machine Learning Research, 23(201), 1-59, 2022.
  Probability Learning PDF
• Concentration inequality for U-statistics of order two for uniformly ergodic Markov chains.
  Y. De Castro, Q. Duchemin, C. Lacour.
  Bernoulli Journal, 2022.
  Probability Learning PDF
• SuperMix: Sparse Regularization for Mixtures.
  Y. De Castro, S. Gadat, C. Marteau, C. Maugis-Rabusseau.
  Annals of Statistics, 49(3), 1779-1809, 2021.
  Learning Optimization PDF
• Dual optimal design and the Christoffel-Darboux polynomial.
  Y. De Castro, F. Gamboa, D. Henrion, J.-B. Lasserre.
  Optimization Letters, 15, 3-8, 2021.
  Design Optimization PDF
• Forecasting Nonnegative Time Series via Sliding Mask Method (SMM).
  Y. De Castro, L. Mencarelli.
  ArXiv Preprint, 2021.
  Learning Probability PDF
• Testing Gaussian Process with Applications to Super-Resolution.
  Y. De Castro, J.-M. Azaïs, S. Mourareau.
  ACHA, 48, 445-481, 2020.
  Learning Probability PDF Code
• Latent distance estimation for random geometric graphs.
  Y. De Castro, E. Araya.
  NeurIPS, 2019.
  Geometry Probability PDF PMLR
• On Representer Theorems and Convex Regularization.
  Y. De Castro, C. Boyer, A. Chambolle, V. Duval, F. de Gournay, P. Weiss.
  SIAM Journal on Optimization, 29(2), 2019.
  Optimization Learning PDF
• Approximate Optimal Designs for Multivariate Polynomial Regression.
  Y. De Castro, F. Gamboa, D. Henrion, R. Hess, J.-B. Lasserre.
  Annals of Statistics, 47(1), 127–155, 2019.
  Algebra Optimization PDF Code
• Nonnegative Matrix Factorization with Side Information for Time Series Recovery.
  Y. De Castro, J-M Azaïs, Y. Goude, G. Hébrail, J. Mei.
  IEEE Trans. Knowledge and Data Engineering, 31(3), 2019.
  Learning Probability PDF
• Sparse Recovery from Extreme Eigenvalues Deviation Inequalities.
  Y. De Castro, S. Dallaporta.
  ESAIM P&S, 23, 430-463, 2019.
  Probability Learning PDF
• A short introduction to Moment-SoS hierarchies
  Y. De Castro.
  Deuxième Congrès National de la SMF, 2018.
  Algebra Optimization PDF
• Adaptive Estimation of Nonparametric Geometric Graphs.
  Y. De Castro, C. Lacour, T. M. Pham Ngoc.
  Mathematical Statistics and Learning, 2(3/4), 2019.
  Graphs Probability PDF
• Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates.
  Jiali Mei, Yohann De Castro, Yannig Goude, Georges Hébrail.
  ICML, 2017.
  Optimization Learning PDF PMLR
• Power of the Spacing test for Least-Angle Regression.
  Y. De Castro, J.-M. Azaïs, S. Mourareau.
  Bernoulli Journal, 24(1), 2018.
  Probability Learning PDF
• Exact solutions to Super Resolution on semi-algebraic domains in higher dimensions.
  Y. De Castro, F. Gamboa, D. Henrion, J.-B. Lasserre.
  IEEE Trans. Info. Theory, 63(1), 2017.
  Algebra Optimization PDF Code
• Adapting to Unknown Noise Level in Sparse Deconvolution.
  Y. De Castro, C. Boyer, J. Salmon.
  Information & Inference, 6(3), 2017.
  Optimization Learning PDF
• Reconstructing undirected graphs from eigenspaces.
  Y. De Castro, T. Espinasse, P. Rochet.
  JMLR, 18(51), 2017.
  Graphs Algebra PDF
• A Rice method proof of the Null-Space Property over the Grassmannian.
  Y. De Castro, J.-M. Azaïs, S. Mourareau.
  Annales de l'Institut Henri Poincaré, 53(4), 2017.
  Probability Geometry PDF
• Minimax adaptive estimation of non-parametric Hidden Markov Models.
  Y. De Castro, É. Gassiat, C. Lacour.
  JMLR, 17(111), 2016.
  Probability Learning PDF Code
• Non-uniform spline recovery from small degree polynomial approximation
  Yohann De Castro and Guillaume Mijoule.
  Journal of Mathematical Analysis and Applications, Pages 971-992, 2015.
  Optimization Learning PDF
• Spike Detection from Inaccurate Samplings.
  Y. De Castro, J.-M. Azaïs, F. Gamboa.
  ACHA, 38(2), 2015.
  Optimization Learning PDF
• Randomized pick-freeze for sparse Sobol indices estimation in high dimension.
  Y. De Castro, A. Janon.
  ESAIM P&S, 19, 2015.
  Probability Learning PDF
• Optimal designs for Lasso and Dantzig selector using Expander Codes.
  Y. De Castro.
  IEEE Trans. Info. Theory, 60(11), 2014.
  Design Optimization PDF
• Exact Reconstruction using Beurling Minimal Extrapolation.
  Y. De Castro, F. Gamboa.
  JMAA, 395(1), 2012.
  Learning Optimization PDF

Ph.D. Students

Post-Docs

Office Hours: Monday 1pm-3pm, Room E6203.

• Machine Learning (2nd year -- Master 1)
  More detail at machine-learning-centrale.github.io .
• Convexity and Parsimony (3rd year -- Master 2)
  More detail at statistical-learning-centrale.github.io .