Artikler med krav om offentlig adgang - Chi JinFå flere oplysninger
Tilgængelige et sted: 31
Is Q-learning provably efficient?
C Jin, Z Allen-Zhu, S Bubeck, MI Jordan
Advances in neural information processing systems 31, 2018
Krav: US Department of Defense
Provably efficient reinforcement learning with linear function approximation
C Jin, Z Yang, Z Wang, MI Jordan
Conference on learning theory, 2137-2143, 2020
Krav: US Department of Defense
On gradient descent ascent for nonconvex-concave minimax problems
T Lin, C Jin, M Jordan
International Conference on Machine Learning, 6083-6093, 2020
Krav: US Department of Defense
What is local optimality in nonconvex-nonconcave minimax optimization?
C Jin, P Netrapalli, M Jordan
International conference on machine learning, 4880-4889, 2020
Krav: US Department of Defense
Near-optimal algorithms for minimax optimization
T Lin, C Jin, MI Jordan
Conference on Learning Theory, 2738-2779, 2020
Krav: US Department of Defense
Gradient descent can take exponential time to escape saddle points
SS Du, C Jin, JD Lee, MI Jordan, A Singh, B Poczos
Advances in neural information processing systems 30, 2017
Krav: US National Science Foundation, US Department of Energy, US Department of …
Reward-free exploration for reinforcement learning
C Jin, A Krishnamurthy, M Simchowitz, T Yu
International Conference on Machine Learning, 4870-4879, 2020
Krav: US National Science Foundation
Bellman eluder dimension: New rich classes of rl problems, and sample-efficient algorithms
C Jin, Q Liu, S Miryoosefi
Advances in neural information processing systems 34, 13406-13418, 2021
Krav: US National Science Foundation
On the theory of transfer learning: The importance of task diversity
N Tripuraneni, M Jordan, C Jin
Advances in neural information processing systems 33, 7852-7862, 2020
Krav: US Department of Defense, UK Engineering and Physical Sciences Research Council
On nonconvex optimization for machine learning: Gradients, stochasticity, and saddle points
C Jin, P Netrapalli, R Ge, SM Kakade, MI Jordan
Journal of the ACM (JACM) 68 (2), 1-29, 2021
Krav: US National Science Foundation, US Department of Defense
Sampling can be faster than optimization
YA Ma, Y Chen, C Jin, N Flammarion, MI Jordan
Proceedings of the National Academy of Sciences 116 (42), 20881-20885, 2019
Krav: US Department of Defense
Local maxima in the likelihood of gaussian mixture models: Structural results and algorithmic consequences
C Jin, Y Zhang, S Balakrishnan, MJ Wainwright, MI Jordan
Advances in neural information processing systems 29, 2016
Krav: US National Science Foundation
Near-optimal reinforcement learning with self-play
Y Bai, C Jin, T Yu
Advances in neural information processing systems 33, 2159-2170, 2020
Krav: US National Science Foundation
Learning Adversarial MDPs with Bandit Feedback and Unknown Transition
C Jin, T Jin, H Luo, S Sra, T Yu
arXiv preprint arXiv:1912.01192, 2020
Krav: US National Science Foundation
Sample-efficient reinforcement learning of undercomplete pomdps
C Jin, S Kakade, A Krishnamurthy, Q Liu
Advances in Neural Information Processing Systems 33, 18530-18539, 2020
Krav: US National Science Foundation, US Department of Defense
The power of exploiter: Provable multi-agent rl in large state spaces
C Jin, Q Liu, T Yu
International Conference on Machine Learning, 10251-10279, 2022
Krav: US National Science Foundation, US Department of Defense
Is RLHF more difficult than standard RL? a theoretical perspective
Y Wang, Q Liu, C Jin
Advances in Neural Information Processing Systems 36, 2024
Krav: US National Science Foundation, US Department of Defense
Risk bounds and rademacher complexity in batch reinforcement learning
Y Duan, C Jin, Z Li
International Conference on Machine Learning, 2892-2902, 2021
Krav: US National Science Foundation, US Department of Defense
Provably efficient reinforcement learning with kernel and neural function approximations
Z Yang, C Jin, Z Wang, M Wang, M Jordan
Advances in Neural Information Processing Systems 33, 13903-13916, 2020
Krav: US National Science Foundation, US Department of Defense
Near-optimal representation learning for linear bandits and linear rl
J Hu, X Chen, C Jin, L Li, L Wang
International Conference on Machine Learning, 4349-4358, 2021
Krav: National Natural Science Foundation of China
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