Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ...
arXiv preprint arXiv:2312.11805, 2023
3405 2023 Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ...
arXiv preprint arXiv:2403.05530, 2024
1293 2024 Reinforcement learning for integer programming: Learning to cut Y Tang, S Agrawal, Y Faenza
International conference on machine learning, 9367-9376, 2020
248 2020 Discretizing continuous action space for on-policy optimization Y Tang, S Agrawal
Proceedings of the aaai conference on artificial intelligence 34 (04), 5981-5988, 2020
153 2020 Es-maml: Simple hessian-free meta learning X Song, W Gao, Y Yang, K Choromanski, A Pacchiano, Y Tang
arXiv preprint arXiv:1910.01215, 2019
146 2019 Nash learning from human feedback R Munos, M Valko, D Calandriello, MG Azar, M Rowland, ZD Guo, Y Tang, ...
arXiv preprint arXiv:2312.00886 18, 2023
119 2023 Monte-Carlo tree search as regularized policy optimization JB Grill, F Altché, Y Tang, T Hubert, M Valko, I Antonoglou, R Munos
International Conference on Machine Learning, 3769-3778, 2020
94 2020 Byol-explore: Exploration by bootstrapped prediction Z Guo, S Thakoor, M Pîslar, B Avila Pires, F Altché, C Tallec, A Saade, ...
Advances in neural information processing systems 35, 31855-31870, 2022
74 2022 Generalized preference optimization: A unified approach to offline alignment Y Tang, ZD Guo, Z Zheng, D Calandriello, R Munos, M Rowland, ...
arXiv preprint arXiv:2402.05749, 2024
72 2024 From complexity to simplicity: Adaptive es-active subspaces for blackbox optimization KM Choromanski, A Pacchiano, J Parker-Holder, Y Tang, V Sindhwani
Advances in Neural Information Processing Systems 32, 2019
57 2019 Orthogonal estimation of Wasserstein distances M Rowland, J Hron, Y Tang, K Choromanski, T Sarlos, A Weller
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
52 2019 Learning to Score Behaviors for Guided Policy Optimization A Pacchiano, J Parker-Holder, Y Tang, A Choromanska, K Choromanski, ...
arXiv preprint arXiv:1906.04349, 2019
47 2019 Exploration by distributional reinforcement learning Y Tang, S Agrawal
arXiv preprint arXiv:1805.01907, 2018
44 2018 Provably robust blackbox optimization for reinforcement learning K Choromanski, A Pacchiano, J Parker-Holder, Y Tang, D Jain, Y Yang, ...
CoRR, abs/1903.02993, 2019
43 2019 Boosting trust region policy optimization by normalizing flows policy Y Tang, S Agrawal
arXiv preprint arXiv:1809.10326, 2018
35 2018 Understanding the performance gap between online and offline alignment algorithms Y Tang, DZ Guo, Z Zheng, D Calandriello, Y Cao, E Tarassov, R Munos, ...
arXiv preprint arXiv:2405.08448, 2024
34 2024 Human alignment of large language models through online preference optimisation D Calandriello, D Guo, R Munos, M Rowland, Y Tang, BA Pires, ...
arXiv preprint arXiv:2403.08635, 2024
34 2024 Understanding self-predictive learning for reinforcement learning Y Tang, ZD Guo, PH Richemond, BA Pires, Y Chandak, R Munos, ...
International Conference on Machine Learning, 33632-33656, 2023
34 2023 An analysis of quantile temporal-difference learning M Rowland, R Munos, MG Azar, Y Tang, G Ostrovski, A Harutyunyan, ...
Journal of Machine Learning Research 25 (163), 1-47, 2024
33 2024 Revisiting Peng’s Q( T Kozuno, Y Tang, M Rowland, R Munos, S Kapturowski, W Dabney, ...
International Conference on Machine Learning, 5794-5804, 2021
25 2021 
Rémi MunosFAIR, MetaEmail được xác minh tại inria.fr