| 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 | 132 | 2019 |
| Learning to learn around a common mean G Denevi, C Ciliberto, D Stamos, M Pontil Advances in neural information processing systems 31, 2018 | 102 | 2018 |
| Online-within-online meta-learning G Denevi, D Stamos, C Ciliberto, M Pontil Advances in Neural Information Processing Systems 32, 2019 | 73 | 2019 |
| Incremental learning-to-learn with statistical guarantees G Denevi, C Ciliberto, D Stamos, M Pontil arXiv preprint arXiv:1803.08089, 2018 | 55 | 2018 |
| 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 | 46 | 2020 |
| Conditional meta-learning of linear representations G Denevi, M Pontil, C Ciliberto Advances in Neural Information Processing Systems 35, 253-266, 2022 | 10 | 2022 |
| Iterative algorithms for a non-linear inverse problem in atmospheric lidar G Denevi, S Garbarino, A Sorrentino Inverse Problems 33 (8), 085010, 2017 | 5 | 2017 |
| Online Parameter-Free Learning of Multiple Low Variance Tasks G Denevi, M Pontil, D Stamos Conference on Uncertainty in Artificial Intelligence, 889-898, 2020 | 1 | 2020 |
| Efficient Lifelong Learning Algorithms: Regret Bounds and Statistical Guarantees G Denevi Università degli Studi di Genova & Istituto Italiano di Tecnologia, 2019 | | 2019 |
| Berhu Penalty for Matrix and Tensor Estimation G Denevi, M Donini, M Pontil | | |
Massimiliano PontilIstituto Italiano di Tecnologia & University College LondonE-mail confirmado em iit.it