受强制性开放获取政策约束的文章 - Jiafan He了解详情
可在其他位置公开访问的文章：19 篇
Provably efficient reinforcement learning for discounted mdps with feature mapping
D Zhou, J He, Q Gu
International Conference on Machine Learning, 12793-12802, 2021
强制性开放获取政策： US National Science Foundation
Logarithmic regret for reinforcement learning with linear function approximation
J He, D Zhou, Q Gu
International Conference on Machine Learning, 4171-4180, 2021
强制性开放获取政策： US National Science Foundation
Nearly minimax optimal reinforcement learning for linear markov decision processes
J He, H Zhao, D Zhou, Q Gu
International Conference on Machine Learning, 12790-12822, 2023
强制性开放获取政策： US National Science Foundation
Nearly optimal algorithms for linear contextual bandits with adversarial corruptions
J He, D Zhou, T Zhang, Q Gu
Advances in neural information processing systems 35, 34614-34625, 2022
强制性开放获取政策： US National Science Foundation
Nearly minimax optimal reinforcement learning for discounted MDPs
J He, D Zhou, Q Gu
Advances in Neural Information Processing Systems 34, 2021
强制性开放获取政策： US National Science Foundation
Achieving a fairer future by changing the past
J He, AD Procaccia, CA Psomas, D Zeng
IJCAI'19, 2019
强制性开放获取政策： US National Science Foundation
A simple and provably efficient algorithm for asynchronous federated contextual linear bandits
J He, T Wang, Y Min, Q Gu
Advances in neural information processing systems 35, 4762-4775, 2022
强制性开放获取政策： US National Science Foundation
Learning stochastic shortest path with linear function approximation
Y Min, J He, T Wang, Q Gu
International Conference on Machine Learning, 15584-15629, 2022
强制性开放获取政策： US National Science Foundation
Near-optimal policy optimization algorithms for learning adversarial linear mixture mdps
J He, D Zhou, Q Gu
International Conference on Artificial Intelligence and Statistics, 4259-4280, 2022
强制性开放获取政策： US National Science Foundation
Variance-dependent regret bounds for linear bandits and reinforcement learning: Adaptivity and computational efficiency
H Zhao, J He, D Zhou, T Zhang, Q Gu
The Thirty Sixth Annual Conference on Learning Theory, 4977-5020, 2023
强制性开放获取政策： US National Science Foundation
Uniform-pac bounds for reinforcement learning with linear function approximation
J He, D Zhou, Q Gu
Advances in Neural Information Processing Systems 34, 2021
强制性开放获取政策： US National Science Foundation
Locally differentially private reinforcement learning for linear mixture markov decision processes
C Liao, J He, Q Gu
Asian Conference on Machine Learning, 627-642, 2023
强制性开放获取政策： US National Science Foundation
On the interplay between misspecification and sub-optimality gap in linear contextual bandits
W Zhang, J He, Z Fan, Q Gu
International Conference on Machine Learning, 41111-41132, 2023
强制性开放获取政策： US National Science Foundation
Cooperative multi-agent reinforcement learning: Asynchronous communication and linear function approximation
Y Min, J He, T Wang, Q Gu
International Conference on Machine Learning, 24785-24811, 2023
强制性开放获取政策： US National Science Foundation
On the sample complexity of learning infinite-horizon discounted linear kernel MDPs
Y Chen, J He, Q Gu
International Conference on Machine Learning, 3149-3183, 2022
强制性开放获取政策： US National Science Foundation
Uniform-PAC guarantees for model-based RL with bounded eluder dimension
Y Wu, J He, Q Gu
Uncertainty in Artificial Intelligence, 2304-2313, 2023
强制性开放获取政策： US National Science Foundation
Provably efficient representation selection in low-rank Markov decision processes: from online to offline RL
W Zhang, J He, D Zhou, Q Gu, A Zhang
Uncertainty in Artificial Intelligence, 2488-2497, 2023
强制性开放获取政策： US National Science Foundation
Nearly minimax optimal regret for learning linear mixture stochastic shortest path
Q Di, J He, D Zhou, Q Gu
International Conference on Machine Learning, 7837-7864, 2023
强制性开放获取政策： US National Science Foundation
Optimal online generalized linear regression with stochastic noise and its application to heteroscedastic bandits
H Zhao, D Zhou, J He, Q Gu
International Conference on Machine Learning, 42259-42279, 2023
强制性开放获取政策： US National Science Foundation
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