Volgen
Daniele Calandriello
Daniele Calandriello
Research Scientist, DeepMind
Geverifieerd e-mailadres voor google.com
Titel
Geciteerd door
Geciteerd door
Jaar
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
4502024
Safe policy iteration
M Pirotta, M Restelli, A Pecorino, D Calandriello
International conference on machine learning, 307-315, 2013
1362013
Nash learning from human feedback
R Munos, M Valko, D Calandriello, MG Azar, M Rowland, ZD Guo, Y Tang, ...
arXiv preprint arXiv:2312.00886 18, 2023
1222023
On fast leverage score sampling and optimal learning
A Rudi, D Calandriello, L Carratino, L Rosasco
Advances in Neural Information Processing Systems 31, 2018
1122018
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
932019
Sparse multi-task reinforcement learning
D Calandriello, A Lazaric, M Restelli
Advances in neural information processing systems 27, 2014
912014
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
752024
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
752022
Exact sampling of determinantal point processes with sublinear time preprocessing
M Derezinski, D Calandriello, M Valko
Advances in neural information processing systems 32, 2019
652019
Information-theoretic online memory selection for continual learning
S Sun, D Calandriello, H Hu, A Li, M Titsias
arXiv preprint arXiv:2204.04763, 2022
542022
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
462018
Second-Order Kernel Online Convex Optimization with Adaptive Sketching
D Calandriello, A Lazaric, M Valko
International Conference on Machine Learning, 2017
462017
Efficient second-order online kernel learning with adaptive embedding
D Calandriello, A Lazaric, M Valko
Advances in Neural Information Processing Systems, 2017
422017
Distributed adaptive sampling for kernel matrix approximation
D Calandriello, A Lazaric, M Valko
International Conference on Artificial Intelligence and Statistics, 2017
42*2017
Human alignment of large language models through online preference optimisation
D Calandriello, D Guo, R Munos, M Rowland, Y Tang, BA Pires, ...
arXiv preprint arXiv:2403.08635, 2024
362024
Statistical and computational trade-offs in kernel k-means
D Calandriello, L Rosasco
Advances in neural information processing systems 31, 2018
362018
Understanding the performance gap between online and offline alignment algorithms
Y Tang, DZ Guo, Z Zheng, D Calandriello, Y Cao, E Tarassov, R Munos, ...
arXiv preprint arXiv:2405.08448, 2024
352024
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
342023
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
322020
Physically interactive robogames: Definition and design guidelines
D Martinoia, D Calandriello, A Bonarini
Robotics and Autonomous Systems 61 (8), 739-748, 2013
322013
Het systeem kan de bewerking nu niet uitvoeren. Probeer het later opnieuw.
Artikelen 1–20