1. J. Depersin and G. Lecué
    Robust subgaussian estimator of a mean vector in nearly linear time
    Submitted, 2019.

  2. G. Chinot, G. Lecué and M. Lerasle
    Robust high dimensional learning for Lipschitz and convex losses
    Submitted, 2019.

  3. J. Kwon, G. Lecué and M. Lerasle
    Median of means principle as a divide-and-conquer procedure for robustness, sub-sampling and hyper-parameters tuning
    Submitted, 2018. Python notebook and codes available here

  4. G. Chinot, G. Lecué and M. Lerasle
    Statistical Learning with Lipschitz and convex loss functions
    To appear in Probability theory and related fields. Python notebook available here

  5. M. Lerasle, T. Matthieu, Z. Szabo and G. Lecué
    MONK – Outliers-Robust Mean Embedding Estimation by Median-of-Means
    ICML 2019

  6. G. Lecué, M. Lerasle and T. Matthieu
    Robust classification via MOM minimization
    Under revision in Machine Learning research
    Python notebooks available here

  7. R. Deswarte and G. Lecué
    minimax regularization
    Under revision in Journal of complexity

  8. Ph. Mesnard, C. Enderli and G. Lecué
    Ground clutter processing for airborne radar in a Compressed Sensing context
    CoSeRa 2018: Compressive Sensing Radar.

  9. Ph. Mesnard, C. Enderli and G. Lecué
    Periodic Patterns Frequency Hopping Waveforms : from conventional Matched Filtering to a new Compressed Sensing Approach
    IRS 2018: Internal Radar Symposium.

  10. G. Lecué and M. Lerasle
    Robust Machine Learning by median of means: theory and practice
    To appear in The annals of statistics, 2017.
    Supplementary material available here
    Python notebooks available here

  11. S. Foucart and G. Lecué
    An IHT algorithm for sparse recovery from subexponential measurements
    IEEE Signal Processing Letters (9) 24, 2017.

  12. P. Alquier, V. Cottet and G. Lecué
    Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions
    To appear in The annals of Statistics
    Supplementary material here
    Python notebooks available here

  13. P. Bellec, G. Lecué and A. Tsybakov
    Towards the study of least squares estimators with convex penalty
    In Séminaire et Congrès, number 31. Société mathématique de France, 2017.

  14. G. Lecué and M. Lerasle
    Learning from MOM’s principles: Le Cam’s approach
    To appear in Stochastic processes and their applications

  15. P. Bellec, G. Lecué and A. Tsybakov
    Slope meets Lasso: improved oracle bounds and optimality
    The Annals of Statistics, 46(6B):3603–3642, 2018.

  16. G. Lecué and S. Mendelson
    Regularization and the small-ball method II: complexity dependent error rates
    Journal of Machine Learning Research, 18(146):1−48, 2017.

  17. G. Lecué and S. Mendelson
    Regularization and the small-ball method I: sparse recovery
    The Annals of Statistics, Volume 46, Number 2 (2018), 611-641. Supplementary material here

  18. S. Dirksen, G. Lecué and H. Rauhut.
    On the gap between RIP-properties and sparse recovery conditions
    IEEE Trans. Inform. Theory 64(8):5478 - 5487, 2018.
    Notebooks available at here for exact reconstruction via Douglas Rachford and here for estimation results for quantized measurements.

  19. G. Lecué and S. Mendelson.
    Performance of empirical risk minimization in linear aggregation
    Bernoulli journal 22 (2016), no. 3, 1520–1534.

  20. G. Lecué and S. Mendelson.
    Sparse recovery under weak moment assumptions
    Journal of the European Mathematical society, Volume 19, Issue 3, 2017, pp. 881–904

  21. G. Lecué and S. Mendelson.
    Minimax rates of convergence and the performance of ERM in phase recovery
    Electronic Journal of Probability, 20 (2015) no. 57, 29 pp.

  22. G. Lecué and S. Mendelson.
    Learning Subgaussian classes : Upper and minimax bounds
    To appear in Topics in Learning Theory - Societe Mathématique de France, (S. Boucheron and N. Vayatis Eds.)

  23. G. Lecué.
    Comment to ‘Generic chaining and the l1-penalty’ by Sara van de Geer
    Journal of statistical and planning inference, Volume 143, Issue 6, Pages 1022-1025 (June 2013).

  24. G. Lecué and P. Rigollet.
    Optimal learning with Q-aggregation
    Annals of Statistics, 42 (2014), no. 1, 211-224.

  25. G. Lecué.
    Empirical risk minimization is optimal for the convex aggregation problem
    Bernoulli journal 19 (2013), no. 5B, 2153–2166.

  26. D. Chafaï, O. Guédon, G. Lecué and A. Pajor.
    Interactions between compressed sensing, Random matrices and high-dimensional geometry
    Collection Panoramas et synthèse of the Société mathématique de France. Volume 37 (2012), 182 pages.

  27. G. Lecué and S. Mendelson.
    On the optimality of the empirical risk minimization procedure for the convex aggregation problem
    Annales de l’institut Henri Poincaré Probabilité et Statistiques, 49 (1), p. 288-306, 2013.

  28. G. Lecué and S. Mendelson.
    On the optimality of the aggregate with exponential weights for low temperatures
    Bernoulli journal 19 (2013), no. 2, 646–675.

  29. G. Lecué and S. Mendelson.
    General non-exact oracle inequalities in the unbounded case
    The Annals of Statistics, 40 (2), p. 832-860. 2012. \ Supplementary material to “General non-exact oracle inequalities in the unbounded case

  30. G. Lecué and C. Mitchell.
    Oracle inequalities for cross-validation type procedures
    Electronic journal of statistics, (6), p. 1803-1837. 2012.

  31. S. Gaïffas and G. Lecué.
    Sharp oracle inequalities for high-dimensional matrix prediction
    IEEE Transactions on Information Theory, 57 (10), p. 6942-6957, 2011.

  32. S. Gaïffas and G. Lecué.
    Hyper-sparse optimal aggregation
    Journal of Machine Learning research, 12(Jun):1813-1833, 2011.

  33. G. Lecué and S. Mendelson.
    Sharper lower bounds on the performance of the Empirical Risk Minimization Algorithm
    Bernoulli journal, 16(3), 2010, p. 605-613, 2010.

  34. G. Lecué and S. Mendelson.
    Aggregation via Empirical Risk Minimization
    Probability theory and related fields, Vol. 145, Number 3-4, pp. 591-613. Novembre, 2009.

  35. C. Chesneau and G. Lecué.
    Adapting to Unknown Smoothness by Aggregation of Thresholded Wavelet Estimators
    Statistica Sinica, Vol. 19, Number 4, October 2009, pp. 1407-1418.

  36. K. Bertin and G. Lecué.
    Selection of variables and dimension reduction in high-dimensional non-parametric regression
    Electronic Journal of Statistics, Vol. 2, pp. 1224-1241. 2008.

  37. G. Lecué.
    Classification with minimax fast rates for Classes of Bayes Rules with Sparse Representation
    Electronic Journal of Statistics, Vol. 2, pp. 741-773. 2008.

  38. S. Gaïffas and G. Lecué.
    Optimal rates and adaptation in the Single-Index Model using aggregation
    Electronic Journal of Statistics, Vol. 1, pp. 538-573. 2007.

  39. G. Lecué.
    Suboptimality of Penalized Empirical Risk Minimization In Classification
    20th Annual Conference On Learning Theory, COLT07. Proceedings. Bshouty, Gentile (Eds.). Springer. LNAI 4539, 142-156. Springer.

  40. G. Lecué.
    Optimal rates of aggregation in classification under low noise assumption
    Bernoulli Journal 13(4), p. 1000-1022, 2007.

  41. G. Lecué.
    Simultaneous adaptation to the margin and to complexity in classification
    The Annals of Statistics, Vol. 35, No. 4. p. 1698-1721. August 2007.

  42. G. Lecué.
    Optimal oracle inequality for aggregation of classifiers under low noise condition
    19th Annual Conference On Learning Theory, COLT06. Proceedings. Gabor Lugosi, Hans Ulrich Simon (Eds.). Springer. LNAI 2006, p. 364-378. “Mark Fulk award” for the best student paper.

  43. G. Lecué.
    Lower Bounds and Aggregation in Density Estimation
    Journal of Machine Learning Research, 7(Jun):971-981, 2006.