| Generalization Properties of Learning with Random Features A Rudi, L Rosasco Advances in Neural Information Processing Systems, 2017 | 380 | 2017 |
| Less is More: Nyström Computational Regularization A Rudi, R Camoriano, L Rosasco Advances in Neural Information Processing Systems (NIPS) 2015, 2015 | 353 | 2015 |
| Falkon: An optimal large scale kernel method A Rudi, L Carratino, L Rosasco Advances in neural information processing systems 30, 2017 | 233 | 2017 |
| Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance G Luise, A Rudi, M Pontil, C Ciliberto Advances in Neural Information Processing Systems, 2018 | 143 | 2018 |
| Kernel methods through the roof: handling billions of points efficiently G Meanti, L Carratino, L Rosasco, A Rudi Advances in Neural Information Processing Systems 33, 14410-14422, 2020 | 126 | 2020 |
| Massively scalable Sinkhorn distances via the Nyström method J Altschuler, F Bach, A Rudi, J Niles-Weed Advances in neural information processing systems 32, 2019 | 105 | 2019 |
| Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes L Pillaud-Vivien, A Rudi, F Bach Advances in Neural Information Processing Systems, 2018 | 105 | 2018 |
| On fast leverage score sampling and optimal learning A Rudi, D Calandriello, L Carratino, L Rosasco Advances in Neural Information Processing Systems 31, 2018 | 104 | 2018 |
| Optimal rates for spectral algorithms with least-squares regression over Hilbert spaces J Lin, A Rudi, L Rosasco, V Cevher Applied and Computational Harmonic Analysis 48 (3), 868-890, 2020 | 100 | 2020 |
| Learning with SGD and Random Features L Carratino, A Rudi, L Rosasco Advances in Neural Information Processing Systems, 2018 | 87 | 2018 |
| A consistent regularization approach for structured prediction C Ciliberto, L Rosasco, A Rudi Advances in neural information processing systems 29, 2016 | 84 | 2016 |
| Beyond least-squares: Fast rates for regularized empirical risk minimization through self-concordance U Marteau-Ferey, D Ostrovskii, F Bach, A Rudi Conference on learning theory, 2294-2340, 2019 | 70 | 2019 |
| A general method for the point of regard estimation in 3D space F Pirri, M Pizzoli, A Rudi CVPR 2011, 921-928, 2011 | 67 | 2011 |
| Non-parametric models for non-negative functions U Marteau-Ferey, F Bach, A Rudi Advances in neural information processing systems 33, 12816-12826, 2020 | 50 | 2020 |
| Structured prediction with partial labelling through the infimum loss V Cabannnes, A Rudi, F Bach International Conference on Machine Learning, 1230-1239, 2020 | 49 | 2020 |
| A general framework for consistent structured prediction with implicit loss embeddings C Ciliberto, L Rosasco, A Rudi Journal of Machine Learning Research 21 (98), 1-67, 2020 | 49 | 2020 |
| Finding global minima via kernel approximations A Rudi, U Marteau-Ferey, F Bach Mathematical Programming, 1-82, 2024 | 45 | 2024 |
| Globally convergent newton methods for ill-conditioned generalized self-concordant losses U Marteau-Ferey, F Bach, A Rudi Advances in Neural Information Processing Systems 32, 2019 | 44 | 2019 |
| A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation A Vacher, B Muzellec, A Rudi, F Bach, FX Vialard arXiv preprint arXiv:2101.05380, 2021 | 42 | 2021 |
| NYTRO: When Subsampling Meets Early Stopping T Angles, R Camoriano, A Rudi, L Rosasco arXiv preprint arXiv:1510.05684, 2015 | 40* | 2015 |
Francis BachInria - Ecole Normale SupérieureEmail verificata su inria.fr
