| Optimization with sparsity-inducing penalties F Bach, R Jenatton, J Mairal, G Obozinski Foundations and Trends® in Machine Learning 4 (1), 1-106, 2012 | 1365 | 2012 |
| Structured variable selection with sparsity-inducing norms R Jenatton, JY Audibert, F Bach Journal of Machine Learning Research 12, 2777-2824, 2011 | 688 | 2011 |
| 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 | 560 | 2021 |
| 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 | 538 | 2012 |
| 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 | 527 | 2023 |
| Proximal methods for sparse hierarchical dictionary learning. R Jenatton, J Mairal, G Obozinski, FR Bach ICML 1, 2, 2010 | 477 | 2010 |
| Structured sparsity through convex optimization F Bach, R Jenatton, J Mairal, G Obozinski Statistical Science 27 (4), 450-468, 2012 | 423 | 2012 |
| 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 | 414 | 2020 |
| Proximal methods for hierarchical sparse coding R Jenatton, J Mairal, G Obozinski, F Bach Journal of Machine Learning Research, 2297-2334, 2011 | 412 | 2011 |
| Structured sparse principal component analysis R Jenatton, G Obozinski, F Bach International Conference on Artificial Intelligence and Statistics (AISTATS), 2010 | 403 | 2010 |
| Convex optimization with sparsity-inducing norms F Bach, R Jenatton, J Mairal, G Obozinski | 377 | 2011 |
| 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 | 262 | 2020 |
| Training independent subnetworks for robust prediction M Havasi, R Jenatton, S Fort, JZ Liu, J Snoek, B Lakshminarayanan, ... arXiv preprint arXiv:2010.06610, 2020 | 237 | 2020 |
| Network flow algorithms for structured sparsity J Mairal, R Jenatton, G Obozinski, F Bach Advances in Neural Information Processing Systems (NIPS), 2010 | 204 | 2010 |
| Scalable hyperparameter transfer learning V Perrone, R Jenatton, MW Seeger, C Archambeau Advances in neural information processing systems 31, 2018 | 185 | 2018 |
| Adaptive algorithms for online convex optimization with long-term constraints R Jenatton, J Huang, C Archambeau International Conference on Machine Learning, 402-411, 2016 | 174 | 2016 |
| Multimodal contrastive learning with limoe: the language-image mixture of experts B Mustafa, C Riquelme, J Puigcerver, R Jenatton, N Houlsby Advances in Neural Information Processing Systems 35, 9564-9576, 2022 | 170 | 2022 |
| Convex relaxations for permutation problems F Fogel, R Jenatton, F Bach, A d'Aspremont Advances in neural information processing systems 26, 2013 | 142 | 2013 |
| On mixup regularization L Carratino, M Cissé, R Jenatton, JP Vert Journal of Machine Learning Research 23 (325), 1-31, 2022 | 135 | 2022 |
| Learning search spaces for bayesian optimization: Another view of hyperparameter transfer learning V Perrone, H Shen, MW Seeger, C Archambeau, R Jenatton Advances in neural information processing systems 32, 2019 | 128 | 2019 |
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr