| 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 | 296 | 2024 |
| Safe policy iteration M Pirotta, M Restelli, A Pecorino, D Calandriello International conference on machine learning, 307-315, 2013 | 134 | 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 | 105 | 2018 |
| 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 | 88 | 2019 |
| Sparse multi-task reinforcement learning D Calandriello, A Lazaric, M Restelli Advances in neural information processing systems 27, 2014 | 86 | 2014 |
| 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 | 78 | 2023 |
| 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 | 68 | 2022 |
| Exact sampling of determinantal point processes with sublinear time preprocessing M Derezinski, D Calandriello, M Valko Advances in neural information processing systems 32, 2019 | 63 | 2019 |
| 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 | 50 | 2024 |
| Information-theoretic online memory selection for continual learning S Sun, D Calandriello, H Hu, A Li, M Titsias arXiv preprint arXiv:2204.04763, 2022 | 49 | 2022 |
| Second-Order Kernel Online Convex Optimization with Adaptive Sketching D Calandriello, A Lazaric, M Valko International Conference on Machine Learning, 2017 | 46 | 2017 |
| 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 | 43 | 2018 |
| Distributed adaptive sampling for kernel matrix approximation D Calandriello, A Lazaric, M Valko International Conference on Artificial Intelligence and Statistics, 2017 | 42* | 2017 |
| Efficient second-order online kernel learning with adaptive embedding D Calandriello, A Lazaric, M Valko Advances in Neural Information Processing Systems, 2017 | 41 | 2017 |
| Statistical and computational trade-offs in kernel k-means D Calandriello, L Rosasco Advances in neural information processing systems 31, 2018 | 35 | 2018 |
| Physically interactive robogames: Definition and design guidelines D Martinoia, D Calandriello, A Bonarini Robotics and Autonomous Systems 61 (8), 739-748, 2013 | 33 | 2013 |
| 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 | 32 | 2023 |
| 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 | 30 | 2020 |
| 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 | 29 | 2024 |
| 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 | 26 | 2020 |
Michal ValkoLlama @ Meta Paris & Inria & MVA - Ex: Gemini and BYOL @ Google DeepMindVerified email at meta.com