Daniele Calandriello

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 57

Top Venues

CoRR

NeurIPS

ICML

ICLR

Lille University of Science and Technology, France (PHD)

Education History

Lille University of Science and Technology, France

PHD

Publications

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Demonstration-Regularized RL. ICLR (2024)
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)
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 (2024)
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 (2024)
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 (2024)
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
Multi-turn Reinforcement Learning from Preference Human Feedback. CoRR (2024)
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 (2024)
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)
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 (2024)
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 (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Demonstration-Regularized RL. CoRR (2023)
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 (2023)
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)
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 (2023)
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 (2023)
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 (2023)
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)
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)
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)
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)
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 (2022)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. CoRR (2022)
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 (2022)
Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis K. Titsias
Information-theoretic Online Memory Selection for Continual Learning. ICLR (2022)
Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis K. Titsias
Information-theoretic Online Memory Selection for Continual Learning. CoRR (2022)
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 (2022)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times. ICML (2022)
Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. CoRR (2021)
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. IEEE Robotics Autom. Lett. 6 (4) (2021)
Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. NeurIPS (2021)
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 (2021)
Alberto Maria Metelli, Matteo Pirotta, Daniele Calandriello, Marcello Restelli
Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach. J. Mach. Learn. Res. 22 (2021)
Huiyi Hu, Ang Li, Daniele Calandriello, Dilan Görür
One Pass ImageNet. CoRR (2021)
Daniele Calandriello, Michal Derezinski, Michal Valko
Sampling from a k-DPP without looking at all items. CoRR (2020)
Daniele Calandriello, Michal Derezinski, Michal Valko
Sampling from a k-DPP without looking at all items. NeurIPS (2020)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification. CoRR (2020)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Near-linear time Gaussian process optimization with adaptive batching and resparsification. ICML (2020)
Anqing Duan, Raffaello Camoriano, Diego Ferigo, Yanlong Huang, Daniele Calandriello, Lorenzo Rosasco, Daniele Pucci
Learning to Avoid Obstacles With Minimal Intervention Control. Frontiers Robotics AI 7 (2020)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. COLT (2019)
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret. CoRR (2019)
Anqing Duan, Raffaello Camoriano, Diego Ferigo, Yanlong Huang, Daniele Calandriello, Lorenzo Rosasco, Daniele Pucci
Learning to Sequence Multiple Tasks with Competing Constraints. IROS (2019)
Michal Derezinski, Daniele Calandriello, Michal Valko
Exact sampling of determinantal point processes with sublinear time preprocessing. CoRR (2019)
Daniele Calandriello, Lorenzo Rosasco
Statistical and Computational Trade-Offs in Kernel K-Means. CoRR (2019)
Michal Derezinski, Daniele Calandriello, Michal Valko
Exact sampling of determinantal point processes with sublinear time preprocessing. NeurIPS (2019)
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Distributed Adaptive Sampling for Kernel Matrix Approximation. CoRR (2018)
Daniele Calandriello, Lorenzo Rosasco
Statistical and Computational Trade-Offs in Kernel K-Means. NeurIPS (2018)
Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko
Improved Large-Scale Graph Learning through Ridge Spectral Sparsification. ICML (2018)
Anqing Duan, Raffaello Camoriano, Diego Ferigo, Daniele Calandriello, Lorenzo Rosasco, Daniele Pucci
Constrained DMPs for Feasible Skill Learning on Humanoid Robots. Humanoids (2018)
Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco
On Fast Leverage Score Sampling and Optimal Learning. CoRR (2018)
Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco
On Fast Leverage Score Sampling and Optimal Learning. NeurIPS (2018)
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Distributed Adaptive Sampling for Kernel Matrix Approximation. AISTATS (2017)
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Second-Order Kernel Online Convex Optimization with Adaptive Sketching. ICML (2017)
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Efficient Second-Order Online Kernel Learning with Adaptive Embedding. NIPS (2017)
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Second-Order Kernel Online Convex Optimization with Adaptive Sketching. CoRR (2017)
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting. CoRR (2016)
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Analysis of Nyström method with sequential ridge leverage scores. UAI (2016)
Daniele Calandriello, Alessandro Lazaric, Michal Valko, Ioannis Koutis
Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning. CoRR (2016)
Daniele Calandriello, Alessandro Lazaric, Marcello Restelli
Sparse multi-task reinforcement learning. Intelligenza Artificiale 9 (1) (2015)
Daniele Calandriello, Gang Niu, Masashi Sugiyama
Semi-supervised information-maximization clustering. Neural Networks 57 (2014)
Daniele Calandriello, Alessandro Lazaric, Marcello Restelli
Sparse Multi-Task Reinforcement Learning. NIPS (2014)
Diego Martinoia, Daniele Calandriello, Andrea Bonarini
Physically Interactive Robogames: Definition and design guidelines. Robotics Auton. Syst. 61 (8) (2013)
Daniele Calandriello, Gang Niu, Masashi Sugiyama
Semi-Supervised Information-Maximization Clustering CoRR (2013)
Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello
Safe Policy Iteration. ICML (3) (2013)

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Publication Activity (10 Years)

Top Topics

Top Venues

Daniele Calandriello