Daniele Calandriello
Daniele Calandriello
Research Scientist, DeepMind
Verified email at google.com
Title
Cited by
Cited by
Year
Safe policy iteration
M Pirotta, M Restelli, A Pecorino, D Calandriello
International Conference on Machine Learning, 307-315, 2013
612013
Sparse multi-task reinforcement learning
D Calandriello, A Lazaric, M Restelli
462014
On fast leverage score sampling and optimal learning
A Rudi, D Calandriello, L Carratino, L Rosasco
arXiv preprint arXiv:1810.13258, 2018
442018
Physically interactive robogames: Definition and design guidelines
D Martinoia, D Calandriello, A Bonarini
Robotics and Autonomous Systems 61 (8), 739-748, 2013
322013
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
262019
Distributed adaptive sampling for kernel matrix approximation
D Calandriello, A Lazaric, M Valko
International Conference on Artificial Intelligence and Statistics, 2017
26*2017
Second-Order Kernel Online Convex Optimization with Adaptive Sketching
D Calandriello, A Lazaric, M Valko
International Conference on Machine Learning, 2017
222017
Exact sampling of determinantal point processes with sublinear time preprocessing
M Dereziński, D Calandriello, M Valko
arXiv preprint arXiv:1905.13476, 2019
212019
Efficient second-order online kernel learning with adaptive embedding
D Calandriello, A Lazaric, M Valko
Advances in Neural Information Processing Systems, 2017
192017
Analysis of Nystr÷m method with sequential ridge leverage score sampling
D Calandriello, A Lazaric, M Valko
Proceedings of the Thirty-Second Conference on Uncertainty in Artificialá…, 2016
122016
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
102018
Semi-supervised information-maximization clustering
D Calandriello, G Niu, M Sugiyama
Neural networks 57, 103-111, 2014
102014
Statistical and computational trade-offs in kernel k-means
D Calandriello, L Rosasco
Advances in Neural Information Processing Systems 31, 9357-9367, 2018
72018
Sampling from a -DPP without looking at all items
D Calandriello, M Dereziński, M Valko
arXiv preprint arXiv:2006.16947, 2020
52020
Efficient Sequential Learning in Structured and Constrained Environments
D Calandriello
Inria Lille Nord Europe-Laboratoire CRIStAL-UniversitÚ de Lille, 2017
52017
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
42018
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
32020
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, NeurIPSá…, 2016
22016
Large-scale semi-supervised learning with online spectral graph sparsification
D Calandriello, A Lazaric, M Valko
Resource-Efficient Machine Learning workshop at International Conference oná…, 2015
22015
Analysis of kelner and levin graph sparsification algorithm for a streaming setting
D Calandriello, A Lazaric, M Valko
arXiv preprint arXiv:1609.03769, 2016
12016
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