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Nino Vieillard
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2020 – today
- 2024
- [c10]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 - [i15]Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret:
WARM: On the Benefits of Weight Averaged Reward Models. CoRR abs/2401.12187 (2024) - [i14]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 abs/2406.16768 (2024) - [i13]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 abs/2407.14622 (2024) - 2023
- [c9]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: 6252-6272 - [c8]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: 17135-17175 - [i12]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 abs/2305.13185 (2023) - [i11]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 abs/2306.00186 (2023) - [i10]Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem:
GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. CoRR abs/2306.13649 (2023) - 2022
- [c7]Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning as Anti-exploration. AAAI 2022: 8106-8114 - [c6]Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist:
Implicitly Regularized RL with Implicit Q-values. AISTATS 2022: 1380-1402 - [i9]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 abs/2205.14211 (2022) - 2021
- [c5]Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning with Pseudometric Learning. ICML 2021: 2307-2318 - [i8]Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning with Pseudometric Learning. CoRR abs/2103.01948 (2021) - [i7]Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning as Anti-Exploration. CoRR abs/2106.06431 (2021) - [i6]Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist:
Implicitly Regularized RL with Implicit Q-Values. CoRR abs/2108.07041 (2021) - 2020
- [c4]Nino Vieillard, Olivier Pietquin, Matthieu Geist:
Deep Conservative Policy Iteration. AAAI 2020: 6070-6077 - [c3]Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist:
Momentum in Reinforcement Learning. AISTATS 2020: 2529-2538 - [c2]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 - [c1]Nino Vieillard, Olivier Pietquin, Matthieu Geist:
Munchausen Reinforcement Learning. NeurIPS 2020 - [i5]Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist:
Leverage the Average: an Analysis of Regularization in RL. CoRR abs/2003.14089 (2020) - [i4]Nino Vieillard, Olivier Pietquin, Matthieu Geist:
Munchausen Reinforcement Learning. CoRR abs/2007.14430 (2020)
2010 – 2019
- 2019
- [i3]Nino Vieillard, Olivier Pietquin, Matthieu Geist:
Deep Conservative Policy Iteration. CoRR abs/1906.09784 (2019) - [i2]Nino Vieillard, Olivier Pietquin, Matthieu Geist:
On Connections between Constrained Optimization and Reinforcement Learning. CoRR abs/1910.08476 (2019) - [i1]Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist:
Momentum in Reinforcement Learning. CoRR abs/1910.09322 (2019)
Coauthor Index
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