| Safe policy iteration M Pirotta, M Restelli, A Pecorino, D Calandriello International Conference on Machine Learning, 307-315, 2013 | 61 | 2013 |
| Sparse multi-task reinforcement learning D Calandriello, A Lazaric, M Restelli | 43 | 2014 |
| On fast leverage score sampling and optimal learning A Rudi, D Calandriello, L Carratino, L Rosasco arXiv preprint arXiv:1810.13258, 2018 | 38 | 2018 |
| Physically interactive robogames: Definition and design guidelines D Martinoia, D Calandriello, A Bonarini Robotics and Autonomous Systems 61 (8), 739-748, 2013 | 31 | 2013 |
| Distributed adaptive sampling for kernel matrix approximation D Calandriello, A Lazaric, M Valko International Conference on Artificial Intelligence and Statistics, 2017 | 25* | 2017 |
| Gaussian process optimization with adaptive sketching: Scalable and no regret D Calandriello, L Carratino, A Lazaric, M Valko, L Rosasco 32nd Annual Conference on Learning Theory, 2019 | 24 | 2019 |
| Second-Order Kernel Online Convex Optimization with Adaptive Sketching D Calandriello, A Lazaric, M Valko International Conference on Machine Learning, 2017 | 22 | 2017 |
| Exact sampling of determinantal point processes with sublinear time preprocessing M Dereziński, D Calandriello, M Valko arXiv preprint arXiv:1905.13476, 2019 | 19 | 2019 |
| Efficient second-order online kernel learning with adaptive embedding D Calandriello, A Lazaric, M Valko Advances in Neural Information Processing Systems, 2017 | 18 | 2017 |
| Analysis of Nyström method with sequential ridge leverage score sampling D Calandriello, A Lazaric, M Valko Proceedings of the Thirty-Second Conference on Uncertainty in Artificial …, 2016 | 12 | 2016 |
| Semi-supervised information-maximization clustering D Calandriello, G Niu, M Sugiyama Neural networks 57, 103-111, 2014 | 10 | 2014 |
| Improved large-scale graph learning through ridge spectral sparsification D Calandriello, I Koutis, A Lazaric, M Valko International Conference on Machine Learning, 687--696, 2018 | 9 | 2018 |
| Statistical and Computational Trade-Offs in Kernel K-Means. D Calandriello, L Rosasco NeurIPS, 9379-9389, 2018 | 6 | 2018 |
| Sampling from a -DPP without looking at all items D Calandriello, M Dereziński, M Valko arXiv preprint arXiv:2006.16947, 2020 | 5 | 2020 |
| Efficient Sequential Learning in Structured and Constrained Environments D Calandriello Inria Lille Nord Europe-Laboratoire CRIStAL-Université de Lille, 2017 | 5 | 2017 |
| Constrained DMPs for feasible skill learning on humanoid robots A Duan, R Camoriano, D Ferigo, D Calandriello, L Rosasco, D Pucci 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), 1-6, 2018 | 3 | 2018 |
| Near-linear time Gaussian process optimization with adaptive batching and resparsification D Calandriello, L Carratino, A Lazaric, M Valko, L Rosasco International Conference on Machine Learning, 1295-1305, 2020 | 2 | 2020 |
| Pack only the essentials: Adaptive dictionary learning for kernel ridge regression D Calandriello, A Lazaric, M Valko Adaptive and Scalable Nonparametric Methods in Machine Learning, NeurIPS …, 2016 | 2 | 2016 |
| Large-scale semi-supervised learning with online spectral graph sparsification D Calandriello, A Lazaric, M Valko Resource-Efficient Machine Learning workshop at International Conference on …, 2015 | 2 | 2015 |
| Analysis of kelner and levin graph sparsification algorithm for a streaming setting D Calandriello, A Lazaric, M Valko arXiv preprint arXiv:1609.03769, 2016 | 1 | 2016 |
Alessandro LazaricResearch Scientist, Facebook Artificial Intelligence ResearchVerified email at inria.fr
