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Daniele Calandriello
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2020 – today
- 2024
- [c30]Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rémi Munos, Mark Rowland, Michal Valko, Daniele Calandriello:
A General Theoretical Paradigm to Understand Learning from Human Preferences. AISTATS 2024: 4447-4455 - [c29]Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot:
Unlocking the Power of Representations in Long-term Novelty-based Exploration. ICLR 2024 - [c28]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Demonstration-Regularized RL. ICLR 2024 - [c27]Daniele Calandriello, Zhaohan Daniel Guo, Rémi Munos, Mark Rowland, Yunhao Tang, Bernardo Ávila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot:
Human Alignment of Large Language Models through Online Preference Optimisation. ICML 2024 - [c26]Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares-López, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel:
Decoding-time Realignment of Language Models. ICML 2024 - [c25]Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Côme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot:
Nash Learning from Human Feedback. ICML 2024 - [c24]Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot:
Generalized Preference Optimization: A Unified Approach to Offline Alignment. ICML 2024 - [i32]Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel:
Decoding-time Realignment of Language Models. CoRR abs/2402.02992 (2024) - [i31]Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot:
Generalized Preference Optimization: A Unified Approach to Offline Alignment. CoRR abs/2402.05749 (2024) - [i30]Daniele Calandriello, Daniel Guo, Rémi Munos, Mark Rowland, Yunhao Tang, Bernardo Ávila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot:
Human Alignment of Large Language Models through Online Preference Optimisation. CoRR abs/2403.08635 (2024) - [i29]Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Yuan Cao, Eugene Tarassov, Rémi Munos, Bernardo Ávila Pires, Michal Valko, Yong Cheng, Will Dabney:
Understanding the performance gap between online and offline alignment algorithms. CoRR abs/2405.08448 (2024) - [i28]Lior Shani, Aviv Rosenberg, Asaf B. Cassel, Oran Lang, Daniele Calandriello, Avital Zipori, Hila Noga, Orgad Keller, Bilal Piot, Idan Szpektor, Avinatan Hassidim, Yossi Matias, Rémi Munos:
Multi-turn Reinforcement Learning from Preference Human Feedback. CoRR abs/2405.14655 (2024) - [i27]Pierre Harvey Richemond, Yunhao Tang, Daniel Guo, Daniele Calandriello, Mohammad Gheshlaghi Azar, Rafael Rafailov, Bernardo Ávila Pires, Eugene Tarassov, Lucas Spangher, Will Ellsworth, Aliaksei Severyn, Jonathan Mallinson, Lior Shani, Gil Shamir, Rishabh Joshi, Tianqi Liu, Rémi Munos, Bilal Piot:
Offline Regularised Reinforcement Learning for Large Language Models Alignment. CoRR abs/2405.19107 (2024) - [i26]Wei Xiong, Chengshuai Shi, Jiaming Shen, Aviv Rosenberg, Zhen Qin, Daniele Calandriello, Misha Khalman, Rishabh Joshi, Bilal Piot, Mohammad Saleh, Chi Jin, Tong Zhang, Tianqi Liu:
Building Math Agents with Multi-Turn Iterative Preference Learning. CoRR abs/2409.02392 (2024) - 2023
- [c23]Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko:
Understanding Self-Predictive Learning for Reinforcement Learning. ICML 2023: 33632-33656 - [c22]Daniil Tiapkin
, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard:
Fast Rates for Maximum Entropy Exploration. ICML 2023: 34161-34221 - [c21]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS 2023 - [i25]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard:
Fast Rates for Maximum Entropy Exploration. CoRR abs/2303.08059 (2023) - [i24]Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot:
Unlocking the Power of Representations in Long-term Novelty-based Exploration. CoRR abs/2305.01521 (2023) - [i23]Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos:
A General Theoretical Paradigm to Understand Learning from Human Preferences. CoRR abs/2310.12036 (2023) - [i22]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Demonstration-Regularized RL. CoRR abs/2310.17303 (2023) - [i21]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Model-free Posterior Sampling via Learning Rate Randomization. CoRR abs/2310.18186 (2023) - [i20]Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot:
Nash Learning from Human Feedback. CoRR abs/2312.00886 (2023) - 2022
- [c20]Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis K. Titsias:
Information-theoretic Online Memory Selection for Continual Learning. ICLR 2022 - [c19]Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco:
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. ICML 2022: 2523-2541 - [c18]Zhaohan Guo, Shantanu Thakoor, Miruna Pislar, Bernardo Ávila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot:
BYOL-Explore: Exploration by Bootstrapped Prediction. NeurIPS 2022 - [c17]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard:
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS 2022 - [i19]Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco:
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. CoRR abs/2201.12909 (2022) - [i18]Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis K. Titsias:
Information-theoretic Online Memory Selection for Continual Learning. CoRR abs/2204.04763 (2022) - [i17]Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pislar, Bernardo Ávila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot:
BYOL-Explore: Exploration by Bootstrapped Prediction. CoRR abs/2206.08332 (2022) - [i16]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard:
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR abs/2209.14414 (2022) - [i15]Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko:
Understanding Self-Predictive Learning for Reinforcement Learning. CoRR abs/2212.03319 (2022) - 2021
- [j6]Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli:
Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach. J. Mach. Learn. Res. 22: 97:1-97:83 (2021) - [j5]Diego Ferigo
On the Emergence of Whole-Body Strategies From Humanoid Robot Push-Recovery Learning. IEEE Robotics Autom. Lett. 6(4): 8561-8568 (2021) - [c16]Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco:
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. NeurIPS 2021: 6430-6441 - [i14]Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte, Daniele Calandriello, Silvio Traversaro, Lorenzo Rosasco, Daniele Pucci:
On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning. CoRR abs/2104.14534 (2021) - [i13]Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco:
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. CoRR abs/2106.12231 (2021) - [i12]Huiyi Hu, Ang Li, Daniele Calandriello, Dilan Görür:
One Pass ImageNet. CoRR abs/2111.01956 (2021) - 2020
- [j4]Anqing Duan
Learning to Avoid Obstacles With Minimal Intervention Control. Frontiers Robotics AI 7: 60 (2020) - [c15]Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco:
Near-linear time Gaussian process optimization with adaptive batching and resparsification. ICML 2020: 1295-1305 - [c14]Daniele Calandriello, Michal Derezinski, Michal Valko:
Sampling from a k-DPP without looking at all items. NeurIPS 2020 - [i11]Daniele Calandriello, Luigi Carratino
Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification. CoRR abs/2002.09954 (2020) - [i10]Daniele Calandriello, Michal Derezinski, Michal Valko:
Sampling from a k-DPP without looking at all items. CoRR abs/2006.16947 (2020)
2010 – 2019
- 2019
- [c13]Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco:
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. COLT 2019: 533-557 - [c12]Anqing Duan
Learning to Sequence Multiple Tasks with Competing Constraints. IROS 2019: 2672-2678 - [c11]Michal Derezinski, Daniele Calandriello, Michal Valko:
Exact sampling of determinantal point processes with sublinear time preprocessing. NeurIPS 2019: 11542-11554 - [i9]Daniele Calandriello, Luigi Carratino
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. CoRR abs/1903.05594 (2019) - [i8]Michal Derezinski, Daniele Calandriello, Michal Valko:
Exact sampling of determinantal point processes with sublinear time preprocessing. CoRR abs/1905.13476 (2019) - [i7]Daniele Calandriello, Lorenzo Rosasco:
Statistical and Computational Trade-Offs in Kernel K-Means. CoRR abs/1908.10284 (2019) - 2018
- [c10]Anqing Duan
Constrained DMPs for Feasible Skill Learning on Humanoid Robots. Humanoids 2018: 1-6 - [c9]Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko:
Improved Large-Scale Graph Learning through Ridge Spectral Sparsification. ICML 2018: 687-696 - [c8]Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco:
On Fast Leverage Score Sampling and Optimal Learning. NeurIPS 2018: 5677-5687 - [c7]Daniele Calandriello, Lorenzo Rosasco:
Statistical and Computational Trade-Offs in Kernel K-Means. NeurIPS 2018: 9379-9389 - [i6]Daniele Calandriello, Alessandro Lazaric, Michal Valko:
Distributed Adaptive Sampling for Kernel Matrix Approximation. CoRR abs/1803.10172 (2018) - [i5]Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco:
On Fast Leverage Score Sampling and Optimal Learning. CoRR abs/1810.13258 (2018) - 2017
- [b1]Daniele Calandriello:
Efficient Sequential Learning in Structured and Constrained Environments. (Apprentissage séquentiel efficace dans des environnements structurés avec contraintes). Lille University of Science and Technology, France, 2017 - [c6]Daniele Calandriello, Alessandro Lazaric, Michal Valko:
Distributed Adaptive Sampling for Kernel Matrix Approximation. AISTATS 2017: 1421-1429 - [c5]Daniele Calandriello, Alessandro Lazaric, Michal Valko:
Second-Order Kernel Online Convex Optimization with Adaptive Sketching. ICML 2017: 645-653 - [c4]Daniele Calandriello, Alessandro Lazaric, Michal Valko:
Efficient Second-Order Online Kernel Learning with Adaptive Embedding. NIPS 2017: 6140-6150 - [i4]Daniele Calandriello, Alessandro Lazaric, Michal Valko:
Second-Order Kernel Online Convex Optimization with Adaptive Sketching. CoRR abs/1706.04892 (2017) - 2016
- [c3]Daniele Calandriello, Alessandro Lazaric, Michal Valko:
Analysis of Nyström method with sequential ridge leverage scores. UAI 2016 - [i3]Daniele Calandriello, Alessandro Lazaric, Michal Valko, Ioannis Koutis:
Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning. CoRR abs/1601.05675 (2016) - [i2]Daniele Calandriello, Alessandro Lazaric, Michal Valko:
Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting. CoRR abs/1609.03769 (2016) - 2015
- [j3]Daniele Calandriello, Alessandro Lazaric, Marcello Restelli:
Sparse multi-task reinforcement learning. Intelligenza Artificiale 9(1): 5-20 (2015) - 2014
- [j2]Daniele Calandriello, Gang Niu, Masashi Sugiyama
Semi-supervised information-maximization clustering. Neural Networks 57: 103-111 (2014) - [c2]Daniele Calandriello, Alessandro Lazaric, Marcello Restelli:
Sparse Multi-Task Reinforcement Learning. NIPS 2014: 819-827 - 2013
- [j1]Diego Martinoia, Daniele Calandriello, Andrea Bonarini
Physically Interactive Robogames: Definition and design guidelines. Robotics Auton. Syst. 61(8): 739-748 (2013) - [c1]Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello:
Safe Policy Iteration. ICML (3) 2013: 307-315 - [i1]Daniele Calandriello, Gang Niu, Masashi Sugiyama:
Semi-Supervised Information-Maximization Clustering. CoRR abs/1304.8020 (2013)
Coauthor Index
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last updated on 2024-10-07 01:21 CEST by the dblp team
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