50. G. Lecué and L. Neirac. Learning with a linear loss function. Excess risk and estimation bounds for ERM, minmax MOM and their regularized versions. Applications to robustness in sparse PCA. Submitted 2023.

49. A. Galtier, G. Lecué, T. Piquard and B. Poterszman. Clustering the state of the French economy, a qualified view on financial crises. Submitted 2022.


48. G. Lecué and Z. Shang. A geometrical viewpoint on the benign overfitting property of the minimum l2-norm interpolant estimator. Submitted 2022.


47. A. Galtier, G. Lecué and L. Boulanger. Machine Learning Your Position in the Financial Stability Landscape. **SSRN. August 2022**. Codes available here.


46. J. Depersin and G. Lecué. Optimal robust mean and location estimation via convex programs with respect to any pseudo-norms. Probability theory and related fields. 183, 997-1025 (2022).


 45. J. Depersin and G. Lecué. On the robustness to adversarial corruption and to heavy-tailed data of the Stahel-Donoho median of means.  Information and Inference: a Journal of the IMA, Volume 12, Issue 2, June 2023, Pages 814–850.


44. S. Chrétien, M. Cucuringu, G. Lecué and L. Neirac. Learning with Semi-Definite Programming: new statistical bounds based on fixed point analysis and excess risk curvature. Journal of Machine Learning Research 22 (2021) 1-64


43. J. Depersin and G. Lecué. Robust subgaussian estimator of a mean vector in nearly linear time. Ann. Statist. 50(1): 511-536 (February 2022).


42. G. Chinot, G. Lecué and  M. Lerasle. Robust high dimensional learning for Lipschitz and convex losses. Journal of machine Learning research (233):1−47, 2020. 


41. 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. Electronic journal of statistics, 15, 1, (2021), 1202-1227. Python notebook and codes available here.


40. G. Chinot, G. Lecué and  M. Lerasle. Statistical Learning with Lipschitz and convex loss functions. Probability theory and related fields, (233):1−47, 2020.  Python notebook available here.


39. M. Lerasle, T. Matthieu, Z. Szabo and G. Lecué. MONK -- Outliers-Robust Mean Embedding Estimation by Median-of-Means. ICML 2019. See PMLR for supplementary files and code.


38. G. Lecué, M. Lerasle and T. Matthieu. Robust classification via MOM minimization. Machine Learning research, 109, 8, (2020), 1635-166. Python notebooks available here.


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


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


35. 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.


34. G. Lecué and M. Lerasle. Robust Machine Learning by median of means: theory and practice. The Annals of Statistics, Volume 48, Number 2 (2020), 906-931. Supplementary material available here. Python notebooks available here.


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


32. P. Alquier, V. Cottet and G. Lecué. Estimation bounds and sharp oracle inequalities of regularized procedures with  Lipschitz loss functions. The Annals of Statistics, 47(4):2117-2144, 2019. Supplementary material here. Python notebooks available here.


31. 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.


30. G. Lecué and M. Lerasle. Learning from MOM's principles: Le Cam's approach. Stochastic processes and their applications, Volume 129, Issue 11, November 2019, Pages 4385-4410.


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


28. 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. 


27. 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.


26. 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.


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


24. 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


23. 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.)


21. 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).


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


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


18. 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.


17. 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.


16. 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.


15. 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.


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


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


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


11. 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.


10. 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.


9. 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.


8. 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.


7. 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.


6. 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.


5. 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.


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


3. 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.


2. 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.