Nino Vieillard

According to our database, Nino Vieillard authored at least 14 papers between 2019 and 2024.

Collaborative distances:

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

2024

WARM: On the Benefits of Weight Averaged Reward Models.

Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret

CoRR, 2024

2023

GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models.

Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem

CoRR, 2023

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice.

Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo

Proceedings of the International Conference on Machine Learning, 2023

Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback.

Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Léonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos Garea, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal.

Tadashi Kozuno, Wenhao Yang, Nino Vieillard, Toshinori Kitamura, Yunhao Tang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Michal Valko, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári

CoRR, 2022

Implicitly Regularized RL with Implicit Q-values.

Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Offline Reinforcement Learning as Anti-exploration.

Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Offline Reinforcement Learning with Pseudometric Learning.

Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Leverage the Average: an Analysis of Regularization in RL.

Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist

CoRR, 2020

Munchausen Reinforcement Learning.

Nino Vieillard, Olivier Pietquin, Matthieu Geist

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

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning.

Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist

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

Momentum in Reinforcement Learning.

Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Deep Conservative Policy Iteration.

Nino Vieillard, Olivier Pietquin, Matthieu Geist

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

On Connections between Constrained Optimization and Reinforcement Learning.

Nino Vieillard, Olivier Pietquin, Matthieu Geist

CoRR, 2019