| Optimization with sparsity-inducing penalties F Bach, R Jenatton, J Mairal, G Obozinski Foundations and Trends® in Machine Learning 4 (1), 1-106, 2012 | 1111 | 2012 |
| Structured variable selection with sparsity-inducing norms R Jenatton, JY Audibert, F Bach Journal of Machine Learning Research 12, 2777-2824, 2011 | 623 | 2011 |
| Proximal methods for sparse hierarchical dictionary learning R Jenatton, J Mairal, G Obozinski, FR Bach ICML, 2010 | 454 | 2010 |
| A latent factor model for highly multi-relational data R Jenatton, N Roux, A Bordes, GR Obozinski Advances in neural information processing systems 25, 2012 | 443 | 2012 |
| Proximal methods for hierarchical sparse coding R Jenatton, J Mairal, G Obozinski, F Bach Journal of Machine Learning Research, 2297-2334, 2011 | 376 | 2011 |
| Structured sparsity through convex optimization F Bach, R Jenatton, J Mairal, G Obozinski Statistical Science 27 (4), 450-468, 2012 | 364 | 2012 |
| Convex optimization with sparsity-inducing norms F Bach, R Jenatton, J Mairal, G Obozinski Optimization for machine learning 5, 19-53, 2011 | 358 | 2011 |
| Structured sparse principal component analysis R Jenatton, G Obozinski, F Bach International Conference on Artificial Intelligence and Statistics (AISTATS), 2010 | 352 | 2010 |
| Network flow algorithms for structured sparsity J Mairal, R Jenatton, G Obozinski, F Bach Advances in Neural Information Processing Systems (NIPS), 2010 | 184 | 2010 |
| How good is the bayes posterior in deep neural networks really? F Wenzel, K Roth, BS Veeling, J Świątkowski, L Tran, S Mandt, J Snoek, ... arXiv preprint arXiv:2002.02405, 2020 | 138 | 2020 |
| Multiscale mining of fMRI data with hierarchical structured sparsity R Jenatton, A Gramfort, V Michel, G Obozinski, E Eger, F Bach, B Thirion SIAM Journal on Imaging Sciences 5 (3), 835-856, 2012 | 118 | 2012 |
| Convex relaxations for permutation problems F Fogel, R Jenatton, F Bach, A d'Aspremont Advances in neural information processing systems 26, 2013 | 115 | 2013 |
| Convex and Network Flow Optimization for Structured Sparsity. J Mairal, R Jenatton, G Obozinski, F Bach Journal of Machine Learning Research 12 (9), 2011 | 106 | 2011 |
| Adaptive algorithms for online convex optimization with long-term constraints R Jenatton, J Huang, C Archambeau International Conference on Machine Learning, 402-411, 2016 | 99 | 2016 |
| Scalable hyperparameter transfer learning V Perrone, R Jenatton, MW Seeger, C Archambeau Advances in neural information processing systems 31, 2018 | 97 | 2018 |
| Sparse and spurious: dictionary learning with noise and outliers R Gribonval, R Jenatton, F Bach IEEE Transactions on Information Theory 61 (11), 6298-6319, 2015 | 91 | 2015 |
| Sample complexity of dictionary learning and other matrix factorizations R Gribonval, R Jenatton, F Bach, M Kleinsteuber, M Seibert IEEE Transactions on Information Theory 61 (6), 3469-3486, 2015 | 91 | 2015 |
| Hyperparameter ensembles for robustness and uncertainty quantification F Wenzel, J Snoek, D Tran, R Jenatton Advances in Neural Information Processing Systems 33, 6514-6527, 2020 | 73 | 2020 |
| Spams: A sparse modeling software, v2. 3 J Mairal, F Bach, J Ponce, G Sapiro, R Jenatton, G Obozinski URL http://spams-devel. gforge. inria. fr/downloads. html, 2014 | 66 | 2014 |
| Training independent subnetworks for robust prediction M Havasi, R Jenatton, S Fort, JZ Liu, J Snoek, B Lakshminarayanan, ... arXiv preprint arXiv:2010.06610, 2020 | 55 | 2020 |
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
