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Daniele Calandriello
Daniele Calandriello
Research Scientist, DeepMind
Dirección de correo verificada de google.com
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A general theoretical paradigm to understand learning from human preferences
MG Azar, ZD Guo, B Piot, R Munos, M Rowland, M Valko, D Calandriello
International Conference on Artificial Intelligence and Statistics, 4447-4455, 2024
2052024
Safe policy iteration
M Pirotta, M Restelli, A Pecorino, D Calandriello
International conference on machine learning, 307-315, 2013
1312013
On fast leverage score sampling and optimal learning
A Rudi, D Calandriello, L Carratino, L Rosasco
Advances in Neural Information Processing Systems 31, 2018
1012018
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
862019
Sparse multi-task reinforcement learning
D Calandriello, A Lazaric, M Restelli
Advances in neural information processing systems 27, 2014
832014
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
622022
Exact sampling of determinantal point processes with sublinear time preprocessing
M Derezinski, D Calandriello, M Valko
Advances in neural information processing systems 32, 2019
622019
Nash learning from human feedback
R Munos, M Valko, D Calandriello, MG Azar, M Rowland, ZD Guo, Y Tang, ...
arXiv preprint arXiv:2312.00886, 2023
572023
Information-theoretic online memory selection for continual learning
S Sun, D Calandriello, H Hu, A Li, M Titsias
arXiv preprint arXiv:2204.04763, 2022
482022
Second-Order Kernel Online Convex Optimization with Adaptive Sketching
D Calandriello, A Lazaric, M Valko
International Conference on Machine Learning, 2017
452017
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
432018
Distributed adaptive sampling for kernel matrix approximation
D Calandriello, A Lazaric, M Valko
International Conference on Artificial Intelligence and Statistics, 2017
41*2017
Efficient second-order online kernel learning with adaptive embedding
D Calandriello, A Lazaric, M Valko
Advances in Neural Information Processing Systems, 2017
392017
Statistical and computational trade-offs in kernel k-means
D Calandriello, L Rosasco
Advances in neural information processing systems 31, 2018
352018
Generalized preference optimization: A unified approach to offline alignment
Y Tang, ZD Guo, Z Zheng, D Calandriello, R Munos, M Rowland, ...
arXiv preprint arXiv:2402.05749, 2024
332024
Physically interactive robogames: Definition and design guidelines
D Martinoia, D Calandriello, A Bonarini
Robotics and Autonomous Systems 61 (8), 739-748, 2013
332013
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
292020
Understanding self-predictive learning for reinforcement learning
Y Tang, ZD Guo, PH Richemond, BA Pires, Y Chandak, R Munos, ...
International Conference on Machine Learning, 33632-33656, 2023
282023
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
252020
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
172018
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Artículos 1–20