| Quantum machine learning: a classical perspective C Ciliberto, M Herbster, AD Ialongo, M Pontil, A Rocchetto, S Severini, ... Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2018 | 548 | 2018 |
| Differential properties of sinkhorn approximation for learning with wasserstein distance G Luise, A Rudi, M Pontil, C Ciliberto Advances in Neural Information Processing Systems 31, 2018 | 167 | 2018 |
| Learning-to-learn stochastic gradient descent with biased regularization G Denevi, C Ciliberto, R Grazzi, M Pontil International Conference on Machine Learning, 1566-1575, 2019 | 141 | 2019 |
| Learning to learn around a common mean G Denevi, C Ciliberto, D Stamos, M Pontil Advances in neural information processing systems 31, 2018 | 111 | 2018 |
| Convex learning of multiple tasks and their structure C Ciliberto, Y Mroueh, T Poggio, L Rosasco International Conference on Machine Learning, 1548-1557, 2015 | 93 | 2015 |
| A consistent regularization approach for structured prediction C Ciliberto, L Rosasco, A Rudi Advances in neural information processing systems 29, 2016 | 87 | 2016 |
| Sinkhorn barycenters with free support via frank-wolfe algorithm G Luise, S Salzo, M Pontil, C Ciliberto Advances in neural information processing systems 32, 2019 | 80 | 2019 |
| Online-within-online meta-learning G Denevi, D Stamos, C Ciliberto, M Pontil Advances in Neural Information Processing Systems 32, 2019 | 78 | 2019 |
| Random expert distillation: Imitation learning via expert policy support estimation R Wang, C Ciliberto, PV Amadori, Y Demiris International Conference on Machine Learning, 6536-6544, 2019 | 76 | 2019 |
| Teaching icub to recognize objects using deep convolutional neural networks G Pasquale, C Ciliberto, F Odone, L Rosasco, L Natale Machine Learning for Interactive Systems, 21-25, 2015 | 70 | 2015 |
| Learning dynamical systems via Koopman operator regression in reproducing kernel Hilbert spaces V Kostic, P Novelli, A Maurer, C Ciliberto, L Rosasco, M Pontil Advances in Neural Information Processing Systems 35, 4017-4031, 2022 | 69 | 2022 |
| Active perception: Building objects' models using tactile exploration N Jamali, C Ciliberto, L Rosasco, L Natale 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids …, 2016 | 68 | 2016 |
| Object identification from few examples by improving the invariance of a deep convolutional neural network G Pasquale, C Ciliberto, L Rosasco, L Natale 2016 IEEE/RSJ international conference on intelligent robots and systems …, 2016 | 68 | 2016 |
| Exploiting mmd and sinkhorn divergences for fair and transferable representation learning L Oneto, M Donini, G Luise, C Ciliberto, A Maurer, M Pontil Advances in Neural Information Processing Systems 33, 15360-15370, 2020 | 63 | 2020 |
| Incremental learning-to-learn with statistical guarantees G Denevi, C Ciliberto, D Stamos, M Pontil arXiv preprint arXiv:1803.08089, 2018 | 57 | 2018 |
| 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 | 55 | 2020 |
| Are we done with object recognition? The iCub robot’s perspective G Pasquale, C Ciliberto, F Odone, L Rosasco, L Natale Robotics and Autonomous Systems 112, 260-281, 2019 | 54 | 2019 |
| Incremental robot learning of new objects with fixed update time R Camoriano, G Pasquale, C Ciliberto, L Natale, L Rosasco, G Metta 2017 IEEE International Conference on Robotics and Automation (ICRA), 3207-3214, 2017 | 50 | 2017 |
| The advantage of conditional meta-learning for biased regularization and fine tuning G Denevi, M Pontil, C Ciliberto Advances in Neural Information Processing Systems 33, 964-974, 2020 | 49 | 2020 |
| Consistent multitask learning with nonlinear output relations C Ciliberto, A Rudi, L Rosasco, M Pontil Advances in Neural Information Processing Systems 30, 2017 | 40 | 2017 |
Massimiliano PontilIstituto Italiano di Tecnologia & University College London在 iit.it 的电子邮件经过验证