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Lin F. Yang (杨林)
Lin F. Yang (杨林)
Assistant Professor, Department of Electrical and Computer Engineering @ UCLA
Verifierad e-postadress på ee.ucla.edu - Startsida
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Sample-optimal parametric q-learning using linearly additive features
L Yang, M Wang
International conference on machine learning, 6995-7004, 2019
3832019
Model-based reinforcement learning with value-targeted regression
A Ayoub, Z Jia, C Szepesvari, M Wang, L Yang
International Conference on Machine Learning, 463-474, 2020
3462020
Reinforcement learning in feature space: Matrix bandit, kernels, and regret bound
L Yang, M Wang
International Conference on Machine Learning, 10746-10756, 2020
3402020
Near-optimal time and sample complexities for solving Markov decision processes with a generative model
A Sidford, M Wang, X Wu, L Yang, Y Ye
Advances in Neural Information Processing Systems 31, 2018
2772018
Is a good representation sufficient for sample efficient reinforcement learning?
SS Du, SM Kakade, R Wang, LF Yang
arXiv preprint arXiv:1910.03016, 2019
2522019
Model-based reinforcement learning with a generative model is minimax optimal
A Agarwal, S Kakade, LF Yang
Conference on Learning Theory, 67-83, 2020
237*2020
Reinforcement learning with general value function approximation: Provably efficient approach via bounded eluder dimension
R Wang, RR Salakhutdinov, L Yang
Advances in Neural Information Processing Systems 33, 6123-6135, 2020
1852020
Model-based multi-agent rl in zero-sum markov games with near-optimal sample complexity
K Zhang, SM Kakade, T Basar, LF Yang
Journal of Machine Learning Research 24 (175), 1-53, 2023
1592023
On reward-free reinforcement learning with linear function approximation
R Wang, SS Du, L Yang, RR Salakhutdinov
Advances in neural information processing systems 33, 17816-17826, 2020
1322020
The hierarchical nature of the spin alignment of dark matter haloes in filaments
MA Aragon-Calvo, LF Yang
Monthly Notices of the Royal Astronomical Society: Letters 440 (1), L46-L50, 2014
1142014
Toward the fundamental limits of imitation learning
N Rajaraman, L Yang, J Jiao, K Ramchandran
Advances in Neural Information Processing Systems 33, 2914-2924, 2020
912020
Solving discounted stochastic two-player games with near-optimal time and sample complexity
A Sidford, M Wang, L Yang, Y Ye
International Conference on Artificial Intelligence and Statistics, 2992-3002, 2020
852020
Q-learning with logarithmic regret
K Yang, L Yang, S Du
International Conference on Artificial Intelligence and Statistics, 1576-1584, 2021
762021
Model-based reinforcement learning with value-targeted regression
Z Jia, L Yang, C Szepesvari, M Wang
Learning for Dynamics and Control, 666-686, 2020
732020
On improving model-free algorithms for decentralized multi-agent reinforcement learning
W Mao, L Yang, K Zhang, T Basar
International Conference on Machine Learning, 15007-15049, 2022
722022
Preference-based reinforcement learning with finite-time guarantees
Y Xu, R Wang, L Yang, A Singh, A Dubrawski
Advances in Neural Information Processing Systems 33, 18784-18794, 2020
672020
Provably efficient reinforcement learning with general value function approximation
R Wang, R Salakhutdinov, LF Yang
arXiv preprint arXiv:2005.10804, 2020
652020
Warmth elevating the depths: shallower voids with warm dark matter
LF Yang, MC Neyrinck, MA Aragón-Calvo, B Falck, J Silk
Monthly Notices of the Royal Astronomical Society 451 (4), 3606-3614, 2015
652015
Clustering high dimensional dynamic data streams
V Braverman, G Frahling, H Lang, C Sohler, LF Yang
International Conference on Machine Learning, 576-585, 2017
622017
Feature-based q-learning for two-player stochastic games
Z Jia, LF Yang, M Wang
arXiv preprint arXiv:1906.00423, 2019
602019
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Artiklar 1–20