Követés
Chengzhuo Ni
Chengzhuo Ni
E-mail megerősítve itt: alumni.princeton.edu
Cím
Hivatkozott rá
Hivatkozott rá
Év
On the convergence and sample efficiency of variance-reduced policy gradient method
J Zhang, C Ni, C Szepesvari, M Wang
Advances in Neural Information Processing Systems 34, 2228-2240, 2021
832021
Reward-directed conditional diffusion: Provable distribution estimation and reward improvement
H Yuan, K Huang, C Ni, M Chen, M Wang
Advances in Neural Information Processing Systems 36, 60599-60635, 2023
352023
Learning to control in metric space with optimal regret
C Ni, LF Yang, M Wang
2019 57th Annual Allerton Conference on Communication, Control, and …, 2019
332019
Off-policy fitted q-evaluation with differentiable function approximators: Z-estimation and inference theory
R Zhang, X Zhang, C Ni, M Wang
International Conference on Machine Learning, 26713-26749, 2022
212022
Representation learning for low-rank general-sum markov games
C Ni, Y Song, X Zhang, Z Ding, C Jin, M Wang
The Eleventh International Conference on Learning Representations, 2023
18*2023
Learning good state and action representations for Markov decision process via tensor decomposition
C Ni, Y Duan, M Dahleh, M Wang, AR Zhang
Journal of Machine Learning Research 24 (115), 1-53, 2023
15*2023
Diffusion model for data-driven black-box optimization
Z Li, H Yuan, K Huang, C Ni, Y Ye, M Chen, M Wang
arXiv preprint arXiv:2403.13219, 2024
102024
Bandit theory and thompson sampling-guided directed evolution for sequence optimization
H Yuan, C Ni, H Wang, X Zhang, L Cong, C Szepesvári, M Wang
Advances in Neural Information Processing Systems 35, 38291-38304, 2022
62022
Optimal estimation of policy gradient via double fitted iteration
C Ni, R Zhang, X Ji, X Zhang, M Wang
International Conference on Machine Learning, 16724-16783, 2022
6*2022
Maximum likelihood tensor decomposition of Markov decision process
C Ni, M Wang
2019 IEEE International Symposium on Information Theory (ISIT), 3062-3066, 2019
52019
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions
Y Wu, JC Kim, C Ni, L Cong, M Wang
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Cikkek 1–11