Daniele Calandriello

74 papers A* 25A 4Journal 40Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Accelerating Nash Learning from Human Feedback via Mirror Prox. CoRR Daniil Tiapkin, Daniele Calandriello, Denis Belomestny, Eric Moulines, Alexey Naumov, Kashif Rasul, Michal Valko, Pierre Ménard
2025	A*	`conf`	Building Math Agents with Multi-Turn Iterative Preference Learning. ICLR 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
2025	A*	`conf`	On Teacher Hacking in Language Model Distillation. ICML Daniil Tiapkin, Daniele Calandriello, Johan Ferret, Sarah Perrin, Nino Vieillard, Alexandre Ramé, Mathieu Blondel
2025	J	`jnl`	On Teacher Hacking in Language Model Distillation. CoRR Daniil Tiapkin, Daniele Calandriello, Johan Ferret, Sarah Perrin, Nino Vieillard, Alexandre Ramé, Mathieu Blondel
2024	A	`conf`	A General Theoretical Paradigm to Understand Learning from Human Preferences. AISTATS Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rémi Munos, Mark Rowland, Michal Valko, Daniele Calandriello
2024	J	`jnl`	Building Math Agents with Multi-Turn Iterative Preference Learning. CoRR 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
2024	A*	`conf`	Decoding-time Realignment of Language Models. ICML Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares-López, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel
2024	J	`jnl`	Decoding-time Realignment of Language Models. CoRR Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel
2024	A*	`conf`	Demonstration-Regularized RL. ICLR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2024	A*	`conf`	Generalized Preference Optimization: A Unified Approach to Offline Alignment. ICML Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
2024	J	`jnl`	Generalized Preference Optimization: A Unified Approach to Offline Alignment. CoRR Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
2024	A*	`conf`	Human Alignment of Large Language Models through Online Preference Optimisation. ICML 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
2024	J	`jnl`	Human Alignment of Large Language Models through Online Preference Optimisation. CoRR 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
2024	J	`jnl`	Multi-turn Reinforcement Learning from Preference Human Feedback. CoRR 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
2024	A*	`conf`	Multi-turn Reinforcement Learning with Preference Human Feedback. NeurIPS Lior Shani, Aviv Rosenberg, Asaf Cassel, Oran Lang, Daniele Calandriello, Avital Zipori, Hila Noga, Orgad Keller, Bilal Piot, Idan Szpektor, Avinatan Hassidim, Yossi Matias, Rémi Munos
2024	A*	`conf`	Nash Learning from Human Feedback. ICML Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, 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
2024	J	`jnl`	Offline Regularised Reinforcement Learning for Large Language Models Alignment. CoRR 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
2024	J	`jnl`	Understanding the performance gap between online and offline alignment algorithms. CoRR Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Yuan Cao, Eugene Tarassov, Rémi Munos, Bernardo Ávila Pires, Michal Valko, Yong Cheng, Will Dabney
2024	A*	`conf`	Unlocking the Power of Representations in Long-term Novelty-based Exploration. ICLR Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot
2023	J	`jnl`	A General Theoretical Paradigm to Understand Learning from Human Preferences. CoRR Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos
2023	J	`jnl`	Demonstration-Regularized RL. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	A*	`conf`	Fast Rates for Maximum Entropy Exploration. ICML Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	J	`jnl`	Fast Rates for Maximum Entropy Exploration. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	A*	`conf`	Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	J	`jnl`	Model-free Posterior Sampling via Learning Rate Randomization. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	J	`jnl`	Nash Learning from Human Feedback. CoRR 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
2023	A*	`conf`	Understanding Self-Predictive Learning for Reinforcement Learning. ICML 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
2023	J	`jnl`	Unlocking the Power of Representations in Long-term Novelty-based Exploration. CoRR Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot
2022	A*	`conf`	BYOL-Explore: Exploration by Bootstrapped Prediction. NeurIPS 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
2022	J	`jnl`	BYOL-Explore: Exploration by Bootstrapped Prediction. CoRR 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
2022	A*	`conf`	Information-theoretic Online Memory Selection for Continual Learning. ICLR Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis K. Titsias
2022	J	`jnl`	Information-theoretic Online Memory Selection for Continual Learning. CoRR Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis K. Titsias
2022	A*	`conf`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2022	J	`jnl`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2022	A*	`conf`	Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. ICML Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2022	J	`jnl`	Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. CoRR Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2022	J	`jnl`	Understanding Self-Predictive Learning for Reinforcement Learning. CoRR 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
2021	J	`jnl`	On the Emergence of Whole-Body Strategies From Humanoid Robot Push-Recovery Learning. IEEE Robotics Autom. Lett. Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte, Daniele Calandriello, Silvio Traversaro, Lorenzo Rosasco, Daniele Pucci
2021	J	`jnl`	On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning. CoRR Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte, Daniele Calandriello, Silvio Traversaro, Lorenzo Rosasco, Daniele Pucci
2021	J	`jnl`	One Pass ImageNet. CoRR Huiyi Hu, Ang Li, Daniele Calandriello, Dilan Görür
2021	A*	`conf`	ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. NeurIPS Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco
2021	J	`jnl`	ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. CoRR Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco
2021	J	`jnl`	Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach. J. Mach. Learn. Res. Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli
2020	J	`jnl`	Learning to Avoid Obstacles With Minimal Intervention Control. Frontiers Robotics AI Anqing Duan, Raffaello Camoriano, Diego Ferigo, Yanlong Huang, Daniele Calandriello, Lorenzo Rosasco, Daniele Pucci
2020	J	`jnl`	Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification. CoRR Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2020	A*	`conf`	Near-linear time Gaussian process optimization with adaptive batching and resparsification. ICML Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2020	A*	`conf`	Sampling from a k-DPP without looking at all items. NeurIPS Daniele Calandriello, Michal Derezinski, Michal Valko
2020	J	`jnl`	Sampling from a k-DPP without looking at all items. CoRR Daniele Calandriello, Michal Derezinski, Michal Valko
2019	A*	`conf`	Exact sampling of determinantal point processes with sublinear time preprocessing. NeurIPS Michal Derezinski, Daniele Calandriello, Michal Valko
2019	J	`jnl`	Exact sampling of determinantal point processes with sublinear time preprocessing. CoRR Michal Derezinski, Daniele Calandriello, Michal Valko
2019	A*	`conf`	Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. COLT Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2019	J	`jnl`	Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. CoRR Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2019	A	`conf`	Learning to Sequence Multiple Tasks with Competing Constraints. IROS Anqing Duan, Raffaello Camoriano, Diego Ferigo, Yanlong Huang, Daniele Calandriello, Lorenzo Rosasco, Daniele Pucci
2019	J	`jnl`	Statistical and Computational Trade-Offs in Kernel K-Means. CoRR Daniele Calandriello, Lorenzo Rosasco
2018	—	`conf`	Constrained DMPs for Feasible Skill Learning on Humanoid Robots. Humanoids Anqing Duan, Raffaello Camoriano, Diego Ferigo, Daniele Calandriello, Lorenzo Rosasco, Daniele Pucci
2018	J	`jnl`	Distributed Adaptive Sampling for Kernel Matrix Approximation. CoRR Daniele Calandriello, Alessandro Lazaric, Michal Valko
2018	A*	`conf`	Improved Large-Scale Graph Learning through Ridge Spectral Sparsification. ICML Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko
2018	A*	`conf`	On Fast Leverage Score Sampling and Optimal Learning. NeurIPS Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco
2018	J	`jnl`	On Fast Leverage Score Sampling and Optimal Learning. CoRR Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco
2018	A*	`conf`	Statistical and Computational Trade-Offs in Kernel K-Means. NeurIPS Daniele Calandriello, Lorenzo Rosasco
2017	A	`conf`	Distributed Adaptive Sampling for Kernel Matrix Approximation. AISTATS Daniele Calandriello, Alessandro Lazaric, Michal Valko
2017	—	`conf`	Efficient Second-Order Online Kernel Learning with Adaptive Embedding. NIPS Daniele Calandriello, Alessandro Lazaric, Michal Valko
2017	—	`—`	Efficient Sequential Learning in Structured and Constrained Environments. (Apprentissage séquentiel efficace dans des environnements structurés avec contraintes). Daniele Calandriello
2017	A*	`conf`	Second-Order Kernel Online Convex Optimization with Adaptive Sketching. ICML Daniele Calandriello, Alessandro Lazaric, Michal Valko
2017	J	`jnl`	Second-Order Kernel Online Convex Optimization with Adaptive Sketching. CoRR Daniele Calandriello, Alessandro Lazaric, Michal Valko
2016	J	`jnl`	Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting. CoRR Daniele Calandriello, Alessandro Lazaric, Michal Valko
2016	A	`conf`	Analysis of Nyström method with sequential ridge leverage scores. UAI Daniele Calandriello, Alessandro Lazaric, Michal Valko
2016	J	`jnl`	Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning. CoRR Daniele Calandriello, Alessandro Lazaric, Michal Valko, Ioannis Koutis
2015	J	`jnl`	Sparse multi-task reinforcement learning. Intelligenza Artificiale Daniele Calandriello, Alessandro Lazaric, Marcello Restelli
2014	J	`jnl`	Semi-supervised information-maximization clustering. Neural Networks Daniele Calandriello, Gang Niu, Masashi Sugiyama
2014	—	`conf`	Sparse Multi-Task Reinforcement Learning. NIPS Daniele Calandriello, Alessandro Lazaric, Marcello Restelli
2013	J	`jnl`	Physically Interactive Robogames: Definition and design guidelines. Robotics Auton. Syst. Diego Martinoia, Daniele Calandriello, Andrea Bonarini
2013	—	`conf`	Safe Policy Iteration. ICML (3) Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello
2013	J	`jnl`	Semi-Supervised Information-Maximization Clustering CoRR Daniele Calandriello, Gang Niu, Masashi Sugiyama