Nino Vieillard

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 25

Top Topics

Textual Entailment

Exploration Exploitation Tradeoff

Reinforcement Learning

Constrained Optimization

Top Venues

Publications

Alexandre Ramé, Johan Ferret, Nino Vieillard, Robert Dadashi, Léonard Hussenot, Pierre-Louis Cedoz, Pier Giuseppe Sessa, Sertan Girgin, Arthur Douillard, Olivier Bachem
WARP: On the Benefits of Weight Averaged Rewarded Policies. CoRR (2024)
Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, Olivier Bachem
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes. ICLR (2024)
Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Nino Vieillard, Alexandre Ramé, Bobak Shahriari, Sarah Perrin, Abe Friesen, Geoffrey Cideron, Sertan Girgin, Piotr Stanczyk, Andrea Michi, Danila Sinopalnikov, Sabela Ramos, Amélie Héliou, Aliaksei Severyn, Matt Hoffman, Nikola Momchev, Olivier Bachem
BOND: Aligning LLMs with Best-of-N Distillation. CoRR (2024)
Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret
WARM: On the Benefits of Weight Averaged Reward Models. CoRR (2024)
Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem
GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. CoRR (2023)
Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Léonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. CoRR (2023)
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
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR (2023)
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
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML (2023)
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
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. ACL (1) (2023)
Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist
Offline Reinforcement Learning as Anti-exploration. AAAI (2022)
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
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR (2022)
Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist
Implicitly Regularized RL with Implicit Q-values. AISTATS (2022)
Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist
Offline Reinforcement Learning with Pseudometric Learning. CoRR (2021)
Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist
Implicitly Regularized RL with Implicit Q-Values. CoRR (2021)
Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist
Offline Reinforcement Learning with Pseudometric Learning. ICML (2021)
Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist
Offline Reinforcement Learning as Anti-Exploration. CoRR (2021)
Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist
Momentum in Reinforcement Learning. AISTATS (2020)
Nino Vieillard, Olivier Pietquin, Matthieu Geist
Munchausen Reinforcement Learning. CoRR (2020)
Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist
Leverage the Average: an Analysis of Regularization in RL. CoRR (2020)
Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. NeurIPS (2020)
Nino Vieillard, Olivier Pietquin, Matthieu Geist
Munchausen Reinforcement Learning. NeurIPS (2020)
Nino Vieillard, Olivier Pietquin, Matthieu Geist
Deep Conservative Policy Iteration. AAAI (2020)
Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist
Momentum in Reinforcement Learning. CoRR (2019)
Nino Vieillard, Olivier Pietquin, Matthieu Geist
On Connections between Constrained Optimization and Reinforcement Learning. CoRR (2019)
Nino Vieillard, Olivier Pietquin, Matthieu Geist
Deep Conservative Policy Iteration. CoRR (2019)