Daniele Calandriello

According to our database, Daniele Calandriello authored at least 46 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Building Math Agents with Multi-Turn Iterative Preference Learning.

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

CoRR, 2024

Offline Regularised Reinforcement Learning for Large Language Models Alignment.

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

CoRR, 2024

Multi-turn Reinforcement Learning from Preference Human Feedback.

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

CoRR, 2024

Understanding the performance gap between online and offline alignment algorithms.

Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Yuan Cao, Eugene Tarassov, Rémi Munos, Bernardo Ávila Pires, Michal Valko, Yong Cheng, Will Dabney

CoRR, 2024

Human Alignment of Large Language Models through Online Preference Optimisation.

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

CoRR, 2024

Generalized Preference Optimization: A Unified Approach to Offline Alignment.

Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Nash Learning from Human Feedback.

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Decoding-time Realignment of Language Models.

Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares-López, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Human Alignment of Large Language Models through Online Preference Optimisation.

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Demonstration-Regularized RL.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Unlocking the Power of Representations in Long-term Novelty-based Exploration.

Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot

Proceedings of the Twelfth International Conference on Learning Representations, 2024

A General Theoretical Paradigm to Understand Learning from Human Preferences.

Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rémi Munos, Mark Rowland, Michal Valko, Daniele Calandriello

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Nash Learning from Human Feedback.

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

CoRR, 2023

A General Theoretical Paradigm to Understand Learning from Human Preferences.

Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, Rémi Munos

CoRR, 2023

Model-free Posterior Sampling via Learning Rate Randomization.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fast Rates for Maximum Entropy Exploration.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard

Proceedings of the International Conference on Machine Learning, 2023

Understanding Self-Predictive Learning for Reinforcement Learning.

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

Proceedings of the International Conference on Machine Learning, 2023

2022

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

BYOL-Explore: Exploration by Bootstrapped Prediction.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times.

Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco

Proceedings of the International Conference on Machine Learning, 2022

Information-theoretic Online Memory Selection for Continual Learning.

Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis K. Titsias

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

On the Emergence of Whole-Body Strategies From Humanoid Robot Push-Recovery Learning.

Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte, Daniele Calandriello, Silvio Traversaro, Lorenzo Rosasco, Daniele Pucci

IEEE Robotics Autom. Lett., 2021

Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach.

Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli

J. Mach. Learn. Res., 2021

One Pass ImageNet.

Huiyi Hu, Ang Li, Daniele Calandriello, Dilan Görür

CoRR, 2021

ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions.

Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Learning to Avoid Obstacles With Minimal Intervention Control.

Anqing Duan, Raffaello Camoriano, Diego Ferigo, Yanlong Huang, Daniele Calandriello, Lorenzo Rosasco, Daniele Pucci

Frontiers Robotics AI, 2020

Sampling from a k-DPP without looking at all items.

Daniele Calandriello, Michal Derezinski, Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Near-linear time Gaussian process optimization with adaptive batching and resparsification.

Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Exact sampling of determinantal point processes with sublinear time preprocessing.

Michal Derezinski, Daniele Calandriello, Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning to Sequence Multiple Tasks with Competing Constraints.

Anqing Duan, Raffaello Camoriano, Diego Ferigo, Yanlong Huang, Daniele Calandriello, Lorenzo Rosasco, Daniele Pucci

Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019

Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret.

Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco

Proceedings of the Conference on Learning Theory, 2019

2018

On Fast Leverage Score Sampling and Optimal Learning.

Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Statistical and Computational Trade-Offs in Kernel K-Means.

Daniele Calandriello, Lorenzo Rosasco

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Improved Large-Scale Graph Learning through Ridge Spectral Sparsification.

Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko

Proceedings of the 35th International Conference on Machine Learning, 2018

Constrained DMPs for Feasible Skill Learning on Humanoid Robots.

Anqing Duan, Raffaello Camoriano, Diego Ferigo, Daniele Calandriello, Lorenzo Rosasco, Daniele Pucci

Proceedings of the 18th IEEE-RAS International Conference on Humanoid Robots, 2018

2017

Efficient Sequential Learning in Structured and Constrained Environments. (Apprentissage séquentiel efficace dans des environnements structurés avec contraintes).

Daniele Calandriello

PhD thesis, 2017

Efficient Second-Order Online Kernel Learning with Adaptive Embedding.

Daniele Calandriello, Alessandro Lazaric, Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Second-Order Kernel Online Convex Optimization with Adaptive Sketching.

Daniele Calandriello, Alessandro Lazaric, Michal Valko

Proceedings of the 34th International Conference on Machine Learning, 2017

Distributed Adaptive Sampling for Kernel Matrix Approximation.

Daniele Calandriello, Alessandro Lazaric, Michal Valko

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning.

Daniele Calandriello, Alessandro Lazaric, Michal Valko, Ioannis Koutis

CoRR, 2016

Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting.

Daniele Calandriello, Alessandro Lazaric, Michal Valko

CoRR, 2016

Analysis of Nyström method with sequential ridge leverage scores.

Daniele Calandriello, Alessandro Lazaric, Michal Valko

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

2015

Sparse multi-task reinforcement learning.

Daniele Calandriello, Alessandro Lazaric, Marcello Restelli

Intelligenza Artificiale, 2015

2014

Semi-supervised information-maximization clustering.

Daniele Calandriello, Gang Niu, Masashi Sugiyama

Neural Networks, 2014

2013

Physically Interactive Robogames: Definition and design guidelines.

Diego Martinoia, Daniele Calandriello, Andrea Bonarini

Robotics Auton. Syst., 2013

Safe Policy Iteration.

Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello

Proceedings of the 30th International Conference on Machine Learning, 2013