| Safe policy iteration M Pirotta, M Restelli, A Pecorino, D Calandriello International Conference on Machine Learning, 307-315, 2013 | 111 | 2013 |
| On fast leverage score sampling and optimal learning A Rudi, D Calandriello, L Carratino, L Rosasco Advances in Neural Information Processing Systems 31, 2018 | 84 | 2018 |
| Sparse multi-task reinforcement learning D Calandriello, A Lazaric, M Restelli Advances in neural information processing systems 27, 2014 | 77 | 2014 |
| 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 | 76 | 2019 |
| Exact sampling of determinantal point processes with sublinear time preprocessing M Derezinski, D Calandriello, M Valko Advances in neural information processing systems 32, 2019 | 55 | 2019 |
| Second-Order Kernel Online Convex Optimization with Adaptive Sketching D Calandriello, A Lazaric, M Valko International Conference on Machine Learning, 2017 | 41 | 2017 |
| Distributed adaptive sampling for kernel matrix approximation D Calandriello, A Lazaric, M Valko International Conference on Artificial Intelligence and Statistics, 2017 | 38* | 2017 |
| Efficient second-order online kernel learning with adaptive embedding D Calandriello, A Lazaric, M Valko Advances in Neural Information Processing Systems, 2017 | 37 | 2017 |
| Byol-explore: Exploration by bootstrapped prediction Z Guo, S Thakoor, M Pîslar, B Avila Pires, F Altché, C Tallec, A Saade, ... Advances in neural information processing systems 35, 31855-31870, 2022 | 36 | 2022 |
| Physically interactive robogames: Definition and design guidelines D Martinoia, D Calandriello, A Bonarini Robotics and Autonomous Systems 61 (8), 739-748, 2013 | 33 | 2013 |
| 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 | 32 | 2018 |
| Information-theoretic online memory selection for continual learning S Sun, D Calandriello, H Hu, A Li, M Titsias arXiv preprint arXiv:2204.04763, 2022 | 31 | 2022 |
| Statistical and computational trade-offs in kernel k-means D Calandriello, L Rosasco Advances in neural information processing systems 31, 2018 | 30 | 2018 |
| Sampling from a k-DPP without looking at all items D Calandriello, M Derezinski, M Valko Advances in Neural Information Processing Systems 33, 6889-6899, 2020 | 24 | 2020 |
| 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 | 22 | 2020 |
| 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 | 17 | 2016 |
| Semi-supervised information-maximization clustering D Calandriello, G Niu, M Sugiyama Neural networks 57, 103-111, 2014 | 15 | 2014 |
| One pass imagenet H Hu, A Li, D Calandriello, D Gorur arXiv preprint arXiv:2111.01956, 2021 | 13 | 2021 |
| Scaling gaussian process optimization by evaluating a few unique candidates multiple times D Calandriello, L Carratino, A Lazaric, M Valko, L Rosasco International Conference on Machine Learning, 2523-2541, 2022 | 12 | 2022 |
| 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 | 12 | 2018 |
Michal ValkoGoogle DeepMind Paris & Inria France & ENS MVAVerified email at deepmind.com
