| On the prediction performance of the lasso AS Dalalyan, M Hebiri, J Lederer Bernoulli 23 (1), 552-581, 2017 | 144 | 2017 |
| The Smooth-Lasso and other ℓ1+ ℓ2-penalized methods M Hebiri, S Van De Geer Electronic Journal of Statistics 5, 1184-1226, 2011 | 121 | 2011 |
| How correlations influence lasso prediction M Hebiri, J Lederer IEEE Transactions on Information Theory 59 (3), 1846-1854, 2012 | 112 | 2012 |
| Some theoretical results on the grouped variables Lasso C Chesneau, M Hebiri Mathematical Methods of Statistics 17 (4), 317-326, 2008 | 71 | 2008 |
| Learning heteroscedastic models by convex programming under group sparsity A Dalalyan, M Hebiri, K Meziani, J Salmon International Conference on Machine Learning, 379-387, 2013 | 28 | 2013 |
| Regularization with the smooth-lasso procedure M Hebiri arXiv preprint arXiv:0803.0668, 2008 | 26 | 2008 |
| Rank-penalized estimation of a quantum system P Alquier, C Butucea, M Hebiri, K Meziani, T Morimae Physical Review A 88 (3), 032113, 2013 | 22 | 2013 |
| Consistency of plug-in confidence sets for classification in semi-supervised learning C Denis, M Hebiri arXiv preprint arXiv:1507.07235, 2015 | 21* | 2015 |
| Leveraging labeled and unlabeled data for consistent fair binary classification E Chzhen, C Denis, M Hebiri, L Oneto, M Pontil arXiv preprint arXiv:1906.05082, 2019 | 17 | 2019 |
| Confidence sets with expected sizes for multiclass classification C Denis, M Hebiri The Journal of Machine Learning Research 18 (1), 3571-3598, 2017 | 16 | 2017 |
| Fair regression via plug-in estimator and recalibration with statistical guarantees E Chzhen, C Denis, M Hebiri, L Oneto, M Pontil NeurIPS 2020-34th Conference on Neural Information Processing Systems, 2020 | 12 | 2020 |
| Fair regression with wasserstein barycenters E Chzhen, C Denis, M Hebiri, L Oneto, M Pontil arXiv preprint arXiv:2006.07286, 2020 | 11 | 2020 |
| Generalization of ℓ1 constraints for high dimensional regression problems P Alquier, M Hebiri Statistics & probability letters 81 (12), 1760-1765, 2011 | 11 | 2011 |
| Sparse conformal predictors M Hebiri Statistics and Computing 20 (2), 253-266, 2010 | 11 | 2010 |
| Transductive versions of the LASSO and the Dantzig Selector P Alquier, M Hebiri Journal of Statistical Planning and Inference 142 (9), 2485-2500, 2012 | 10 | 2012 |
| On lasso refitting strategies E Chzhen, M Hebiri, J Salmon Bernoulli 25 (4A), 3175-3200, 2019 | 8 | 2019 |
| Quelques questions de sélection de variables autour de l'estimateur LASSO M Hebiri Université Paris-Diderot-Paris VII, 2009 | 8 | 2009 |
| Minimax semi-supervised confidence sets for multi-class classification E Chzhen, C Denis, M Hebiri arXiv preprint arXiv:1904.12527, 2019 | 7 | 2019 |
| Layer sparsity in neural networks M Hebiri, J Lederer arXiv preprint arXiv:2006.15604, 2020 | 6 | 2020 |
| On the benefits of output sparsity for multi-label classification E Chzhen, C Denis, M Hebiri, J Salmon arXiv preprint arXiv:1703.04697, 2017 | 6 | 2017 |
