| Optimization with sparsity-inducing penalties F Bach, R Jenatton, J Mairal, G Obozinski Foundations and Trends® in Machine Learning 4 (1), 1-106, 2012 | 1271 | 2012 |
| Structured variable selection with sparsity-inducing norms R Jenatton, JY Audibert, F Bach Journal of Machine Learning Research 12, 2777-2824, 2011 | 669 | 2011 |
| 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 | 507 | 2012 |
| Proximal methods for sparse hierarchical dictionary learning. R Jenatton, J Mairal, G Obozinski, FR Bach ICML 1, 2, 2010 | 471 | 2010 |
| Proximal methods for hierarchical sparse coding R Jenatton, J Mairal, G Obozinski, F Bach Journal of Machine Learning Research, 2297-2334, 2011 | 402 | 2011 |
| Structured sparsity through convex optimization F Bach, R Jenatton, J Mairal, G Obozinski | 395 | 2012 |
| Structured sparse principal component analysis R Jenatton, G Obozinski, F Bach International Conference on Artificial Intelligence and Statistics (AISTATS), 2010 | 392 | 2010 |
| Convex optimization with sparsity-inducing norms F Bach, R Jenatton, J Mairal, G Obozinski Optimization for machine learning 5, 19-53, 2011 | 377 | 2011 |
| 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 | 309 | 2020 |
| Scaling vision with sparse mixture of experts C Riquelme, J Puigcerver, B Mustafa, M Neumann, R Jenatton, ... Advances in Neural Information Processing Systems 34, 8583-8595, 2021 | 256 | 2021 |
| Network flow algorithms for structured sparsity J Mairal, R Jenatton, G Obozinski, F Bach Advances in Neural Information Processing Systems (NIPS), 2010 | 198 | 2010 |
| 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 | 175 | 2020 |
| Scalable hyperparameter transfer learning V Perrone, R Jenatton, MW Seeger, C Archambeau Advances in neural information processing systems 31, 2018 | 158 | 2018 |
| Training independent subnetworks for robust prediction M Havasi, R Jenatton, S Fort, JZ Liu, J Snoek, B Lakshminarayanan, ... arXiv preprint arXiv:2010.06610, 2020 | 148 | 2020 |
| Adaptive algorithms for online convex optimization with long-term constraints R Jenatton, J Huang, C Archambeau International Conference on Machine Learning, 402-411, 2016 | 147 | 2016 |
| Convex relaxations for permutation problems F Fogel, R Jenatton, F Bach, A d'Aspremont Advances in neural information processing systems 26, 2013 | 139 | 2013 |
| Scaling vision transformers to 22 billion parameters M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ... International Conference on Machine Learning, 7480-7512, 2023 | 136 | 2023 |
| 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 | 123 | 2012 |
| Convex and Network Flow Optimization for Structured Sparsity. J Mairal, R Jenatton, G Obozinski, F Bach Journal of Machine Learning Research 12 (9), 2011 | 121 | 2011 |
| 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 | 106 | 2015 |
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
