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Yunhao Tang
Yunhao Tang
Research Scientist, Llama research team; Previously, DeepMind
Geverifieerd e-mailadres voor columbia.edu - Homepage
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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
25562023
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
10312024
Reinforcement learning for integer programming: Learning to cut
Y Tang, S Agrawal, Y Faenza
International conference on machine learning, 9367-9376, 2020
2482020
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
1512020
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
1452019
Nash learning from human feedback
R Munos, M Valko, D Calandriello, MG Azar, M Rowland, ZD Guo, Y Tang, ...
arXiv preprint arXiv:2312.00886, 2023
982023
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
902020
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
732022
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
652024
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
562019
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
512019
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
452019
Exploration by distributional reinforcement learning
Y Tang, S Agrawal
arXiv preprint arXiv:1805.01907, 2018
442018
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
432019
Boosting trust region policy optimization by normalizing flows policy
Y Tang, S Agrawal
arXiv preprint arXiv:1809.10326, 2018
352018
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
342024
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
342023
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, 1-47, 2024
312024
Revisiting Peng’s Q() for Modern Reinforcement Learning
T Kozuno, Y Tang, M Rowland, R Munos, S Kapturowski, W Dabney, ...
International Conference on Machine Learning, 5794-5804, 2021
262021
Self-imitation learning via generalized lower bound q-learning
Y Tang
Advances in neural information processing systems 33, 13964-13975, 2020
242020
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Artikelen 1–20