Safe Policy Iteration. - Matteo Pirotta; Marcello Restelli; Alessio Pecorino; Daniele Calandriello (ICML (3), 2013)

Physically Interactive Robogames: Definition and design guidelines. - Diego Martinoia; Daniele Calandriello; Andrea Bonarini (Robotics Auton. Syst., 2013)

Semi-supervised information-maximization clustering. - Daniele Calandriello; Gang Niu 001; Masashi Sugiyama (Neural Networks, 2014)

Sparse multi-task reinforcement learning. - Daniele Calandriello; Alessandro Lazaric; Marcello Restelli (Intelligenza Artificiale, 2015)

Analysis of Nyström method with sequential ridge leverage scores. - Daniele Calandriello; Alessandro Lazaric; Michal Valko (UAI, 2016)

Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting. - Daniele Calandriello; Alessandro Lazaric; Michal Valko (CoRR, 2016)

Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning. - Daniele Calandriello; Alessandro Lazaric; Michal Valko; Ioannis Koutis (CoRR, 2016)

Distributed Adaptive Sampling for Kernel Matrix Approximation. - Daniele Calandriello; Alessandro Lazaric; Michal Valko (AISTATS, 2017)

Second-Order Kernel Online Convex Optimization with Adaptive Sketching. - Daniele Calandriello; Alessandro Lazaric; Michal Valko (ICML, 2017)

Efficient Second-Order Online Kernel Learning with Adaptive Embedding. - Daniele Calandriello; Alessandro Lazaric; Michal Valko (NIPS, 2017)

Efficient Sequential Learning in Structured and Constrained Environments. (Apprentissage séquentiel efficace dans des environnements structurés avec contraintes). - Daniele Calandriello (2017)

Constrained DMPs for Feasible Skill Learning on Humanoid Robots. - Anqing Duan; Raffaello Camoriano; Diego Ferigo; Daniele Calandriello; Lorenzo Rosasco; Daniele Pucci (Humanoids, 2018)

Improved Large-Scale Graph Learning through Ridge Spectral Sparsification. - Daniele Calandriello; Ioannis Koutis; Alessandro Lazaric; Michal Valko (ICML, 2018)

Statistical and Computational Trade-Offs in Kernel K-Means. - Daniele Calandriello; Lorenzo Rosasco (NeurIPS, 2018)

On Fast Leverage Score Sampling and Optimal Learning. - Alessandro Rudi; Daniele Calandriello; Luigi Carratino; Lorenzo Rosasco (NeurIPS, 2018)

Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. - Daniele Calandriello; Luigi Carratino; Alessandro Lazaric; Michal Valko; Lorenzo Rosasco (COLT, 2019)

Learning to Sequence Multiple Tasks with Competing Constraints. - Anqing Duan; Raffaello Camoriano; Diego Ferigo; Yanlong Huang; Daniele Calandriello; Lorenzo Rosasco; Daniele Pucci (IROS, 2019)

Exact sampling of determinantal point processes with sublinear time preprocessing. - Michal Derezinski; Daniele Calandriello; Michal Valko (NeurIPS, 2019)

Near-linear time Gaussian process optimization with adaptive batching and resparsification. - Daniele Calandriello; Luigi Carratino; Alessandro Lazaric; Michal Valko; Lorenzo Rosasco (ICML, 2020)

Sampling from a k-DPP without looking at all items. - Daniele Calandriello; Michal Derezinski; Michal Valko (NeurIPS, 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)

ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. - Luigi Carratino; Stefano Vigogna; Daniele Calandriello; Lorenzo Rosasco (NeurIPS, 2021)

One Pass ImageNet. - Huiyi Hu; Ang Li; Daniele Calandriello; Dilan Görür (CoRR, 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)

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)

Information-theoretic Online Memory Selection for Continual Learning. - Shengyang Sun; Daniele Calandriello; Huiyi Hu; Ang Li; Michalis K. Titsias (ICLR, 2022)

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. - Daniele Calandriello; Luigi Carratino; Alessandro Lazaric; Michal Valko; Lorenzo Rosasco (ICML, 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; et al. (NeurIPS, 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 001; Michal Valko; Pierre Ménard (NeurIPS, 2022)

Understanding Self-Predictive Learning for Reinforcement Learning. - Yunhao Tang; Zhaohan Daniel Guo; Pierre Harvey Richemond; Bernardo Ávila Pires; Yash Chandak; Rémi Munos; Mark Rowland 001; Mohammad Gheshlaghi Azar; Charline Le Lan; Clare Lyle; András György 001; Shantanu Thakoor; et al. (ICML, 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 (ICML, 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 (NeurIPS, 2023)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Zhaohan Daniel Guo; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Andrea Michi; Marco Selvi; Sertan Girgin; et al. (CoRR, 2023)

A General Theoretical Paradigm to Understand Learning from Human Preferences. - Mohammad Gheshlaghi Azar; Zhaohan Daniel Guo; Bilal Piot; Rémi Munos; Mark Rowland 001; Michal Valko; Daniele Calandriello (AISTATS, 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 (ICLR, 2024)

Demonstration-Regularized RL. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Alexey Naumov; Pierre Perrault; Michal Valko; Pierre Ménard (ICLR, 2024)

Human Alignment of Large Language Models through Online Preference Optimisation. - Daniele Calandriello; Zhaohan Daniel Guo; Rémi Munos; Mark Rowland 001; Yunhao Tang; Bernardo Ávila Pires; Pierre Harvey Richemond; Charline Le Lan; Michal Valko; Tianqi Liu 002; Rishabh Joshi; Zeyu Zheng; et al. (ICML, 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 (ICML, 2024)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Daniel Guo 001; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Côme Fiegel; Andrea Michi; Marco Selvi; et al. (ICML, 2024)

Generalized Preference Optimization: A Unified Approach to Offline Alignment. - Yunhao Tang; Zhaohan Daniel Guo; Zeyu Zheng; Daniele Calandriello; Rémi Munos; Mark Rowland 001; Pierre Harvey Richemond; Michal Valko; Bernardo Ávila Pires; Bilal Piot (ICML, 2024)

Multi-turn Reinforcement Learning with Preference Human Feedback. - Lior Shani; Aviv Rosenberg 002; Asaf Cassel; Oran Lang; Daniele Calandriello; Avital Zipori; Hila Noga; Orgad Keller; Bilal Piot; Idan Szpektor; Avinatan Hassidim; Yossi Matias; et al. (NeurIPS, 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)

Multi-turn Reinforcement Learning from Preference Human Feedback. - Lior Shani; Aviv Rosenberg 002; Asaf B. Cassel; Oran Lang; Daniele Calandriello; Avital Zipori; Hila Noga; Orgad Keller; Bilal Piot; Idan Szpektor; Avinatan Hassidim; Yossi Matias; et al. (CoRR, 2024)

Offline Regularised Reinforcement Learning for Large Language Models Alignment. - Pierre Harvey Richemond; Yunhao Tang; Daniel Guo 001; Daniele Calandriello; Mohammad Gheshlaghi Azar; Rafael Rafailov; Bernardo Ávila Pires; Eugene Tarassov; Lucas Spangher; Will Ellsworth; Aliaksei Severyn; Jonathan Mallinson; et al. (CoRR, 2024)

Building Math Agents with Multi-Turn Iterative Preference Learning. - Wei Xiong 015; Chengshuai Shi; Jiaming Shen; Aviv Rosenberg 002; Zhen Qin 001; Daniele Calandriello; Misha Khalman; Rishabh Joshi; Bilal Piot; Mohammad Saleh; Chi Jin; Tong Zhang 001; et al. (ICLR, 2025)

On Teacher Hacking in Language Model Distillation. - Daniil Tiapkin; Daniele Calandriello; Johan Ferret; Sarah Perrin; Nino Vieillard; Alexandre Ramé; Mathieu Blondel (CoRR, 2025)

Accelerating Nash Learning from Human Feedback via Mirror Prox. - Daniil Tiapkin; Daniele Calandriello; Denis Belomestny; Eric Moulines; Alexey Naumov; Kashif Rasul; Michal Valko; Pierre Ménard (CoRR, 2025)