Yohann De Castro

My (almost complete list of) publications can be found on this list of publications (type and year)

I provide below of list of publications by subjects (Sparse models & Mathematical Statistics)

Sparse models: High-Dimension (L1); Measures (TV); Variational (TV gradient)

• FastPart: Over-Parameterized Stochastic Gradient Descent for Sparse optimisation on Measures with S. Gadat and C. Marteau.
• Exact recovery of the support of piecewise constant images via total variation regularization with V. Duval and R. Petit.
• SIGLE: valid Selective Inference procedure for Generalized Linear Lasso, with Q. Duchemin, arXiv, 2023.
• Towards off-the-grid algorithms for total variation regularized inverse problems, with R. Petit & V. Duval, Proceedings of the eighth international conference on Scale Space and Variational Methods in computer vision (SSVM), Pages 553--564, 2021; and published in Journal of Mathematical Imaging and Vision, 2022 (extended version). GitHub Repository
• Dual optimal design and the Christoffel-Darboux polynomial with F. Gamboa & D. Henrion & J.-B. Lasserre, Optimization Letters, 2021, Vol. 15, 3-8.
• SuperMix: Sparse Regularization for Mixtures, with S. Gadat & C. Marteau & C. Maugis-Rabusseau, Annals of Statistics, 2021, Vol. 49, No. 3, 1779-1809.
• Multiple Testing and Variable Selection along Least Angle Regression's path, with J-M Azaïs, Information & Inference: A Journal of the IMA, 2022. GitHub Repository
• A Short Introduction to “Moment-SoS Hierarchies”, Deuxième Congès National de la SMF, 2018.
• On Representer Theorems and Convex Regularization, with C. Boyer & A. Chambolle & V. Duval & F. de Gournay & P. Weiss, SIAM Journal on Optimization, 29(2), 1260–1281. A related conference paper published at iTWIST'18 is Convex Regularization and Representer Theorems, with C. Boyer & A. Chambolle & V. Duval & F. de Gournay & P. Weiss.
• Testing Gaussian Process with Applications to Super-Resolution, with J.-M. Azaïs & S. Mourareau, Applied and Computational Harmonic Analysis, Volume 48, Pages 445-481, 2020. GitHub Repository
• Approximate Optimal Designs for Multivariate Polynomial Regression with F. Gamboa & D. Henrion & R. Hess & J.-B. Lasserre, Annals of Statistics, Volume 47, Issue 1, Pages 127–155, 2019. Matlab Code
  Extended version of D-Optimal Design for Multivariate Polynomial Regression via the Christoffel function and Semidefinite Relaxations, with F. Gamboa & D. Henrion & R. Hess & J.-B. Lasserre, arXiv 1703.01777.
• Sparse Recovery from Extreme Eigenvalues Deviation Inequalities, with S. Dallaporta, ESAIM P&S, Volume 23, Pages 430-463, 2019.
• Adapting to Unknown Noise Level in Sparse Deconvolution, with C. Boyer & J. Salmon, Information & Inference: A Journal of the IMA, Volume 6, Issue 3, 1 September 2017, Pages 310–348.
• Exact solutions to Super Resolution on semi-algebraic domains in higher dimensions, with F. Gamboa & D. Henrion & J.-B. Lasserre, IEEE Transactions on Information Theory, Volume 63, Issue 1, Jan. 2017, Pages 621-630. Matlab Code
• Power of the Spacing test for Least-Angle Regression, with J.-M. Azaïs & S. Mourareau, Bernoulli Journal, Volume 24 n°1 (2018), Pages 465-492.
• Randomized pick-freeze for sparse Sobol indices estimation in high dimension, with A. Janon, ESAIM P&S, Volume 19, 2015, Pages 725-745.
• Non-uniform spline recovery from small degree polynomial approximation, with G. Mijoule, Journal of Mathematical Analysis and Applications, Volume 430, Issue 2, 15 October 2015, Pages 971–992.
• A Rice method proof of the Null-Space Property over the Grassmannian, with J.-M. Azaïs & S. Mourareau, Annales de l’Institut Henri Poincaré, Probabilités et Statistiques, Volume 53, Number 4 (2017), Pages 1821-1838.
• Spike Detection from Inaccurate Samplings, with J.-M. Azaïs & F. Gamboa, Applied and Computational Harmonic Analysis, Volume 38, Issue 2, March 2015, Pages 177–195.
• Exact Reconstruction using Beurling Minimal Extrapolation, with F. Gamboa, Journal of Mathematical Analysis and Applications, Volume 395, Issue 1, Nov. 2012, Pages 336-354.
• A remark on the lasso and the Dantzig selector, Statistics & Probability Letters, Volume 83, Issue 1, January 2013, Pages 304-314.
• Optimal designs for Lasso and Dantzig selector using Expander Codes, IEEE Transactions on Information Theory, Volume 60, Issue 11, Nov. 2014, Pages 7293-7299.

Mathematical Statistics, Learning, and Applications

• Neural Networks beyond explainability: Selective inference for sequence motifs, with A. Villié, P. Veber, and L. Jacob, Transactions on Machine Learning Research (TMLR), 06/2023. OpenReview
• Minimax Estimation of Partially-Observed Vector AutoRegressions, with G. Dalle, arXiv:2106.09327, submitted, 2021.
• Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF), with L. Mencarelli, arXiv:2102.05314, 2021.
• Random Geometric Graph: Some recent developments and perspectives, with Q. Duchemin, accepted for publication at the proceedings of the High Dimensional Probability IX Conference, Progress in Probability series.
• Three rates of convergence or separation via U-statistics in a dependent framework, with Q. Duchemin and C. Lacour, Journal of Machine Learning Research, Volume 23, Issue 201, Pages 1-59, 2022.
• Concentration inequality for U-statistics of order two for uniformly ergodic Markov chains, with Q. Duchemin and C. Lacour, Bernoulli Journal, 2022 (to appear).
• Markov Random Geometric Graph (MRGG): A Growth Model for Temporal Dynamic Networks, with Q. Duchemin, Electronic Journal of Statistics, Volume 16, Pages 671-699, 2022.
GitHub Repository
• Latent distance estimation for random geometric graphs, with E. Araya, NeurIPS 2019.
• Adaptive Estimation of Nonparametric Geometric Graphs, with C. Lacour & T. M. Pham Ngoc, Mathematical Statistics and Learning, 2020 (to appear).
• Nonnegative Matrix Factorization with Side Information for Time Series Recovery and Prediction, with J-M Azaïs & Y. Goude & G. Hébrail & J. Mei, IEEE Transactions on Knowledge and Data Engineering, Volume 31, Issue 3, March 1, Pages 493-506, 2019.
• Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates, with J-M Azaïs & Y. Goude & G. Hébrail & J. Mei, Proceedings of the 34th International Conference on Machine Learning (ICML 2017), PMLR 70:2382-2390, 2017.
• Reconstructing undirected graphs from eigenspaces, with T. Espinasse & P. Rochet, Journal of Machine Learning Research, Volume 18, Issue 51, Pages 1−24, 2017.
• Consistent estimation of the filtering and marginal smoothing distributions in nonparametric hidden Markov models, with É. Gassiat & S. Le Corff, IEEE Transactions on Information Theory, Volume 63, Issue 8, Aug. 2017, Pages 4758-4777.
• Minimax adaptive estimation of non-parametric Hidden Markov Models, with É. Gassiat & C. Lacour, Journal of Machine Learning Research, Volume 17, Issue 111, Pages 1-43, 2016. Matlab Codes
• Estimating the transition matrix of a Markov chain observed at random times, with F. Barsotti & T. Espinasse & P. Rochet, Statistics & Probability Letters, Volume 94, November 2014, Pages 98-105.
• Quantitative Isoperimetric Inequalities on the Real Line, Annales Mathématiques Blaise Pascal, Volume 18 n°2 (2011), Pages 311-331.

My office hours are on 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 .

• yohann.de-castro@ec-lyon.fr
• +33 (0)4 72 18 67 90
• Institut Camille Jordan
  École Centrale de Lyon
  36 Avenue Guy de Collongue
  69134 Écully, France

• yohann.de-castro (at) ec-lyon.fr
• Yohann's @ECL
• Institut Camille Jordan
  École Centrale de Lyon
  36 Avenue Guy de Collongue
  69134 Écully, France