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2020 – today
- 2024
[i9]Zihao Li, Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Yinyu Ye, Minshuo Chen, Mengdi Wang:
Diffusion Model for Data-Driven Black-Box Optimization. CoRR abs/2403.13219 (2024)- 2023
- [j1]Chengzhuo Ni, Yaqi Duan, Munther A. Dahleh, Mengdi Wang, Anru R. Zhang:
Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition. J. Mach. Learn. Res. 24: 115:1-115:53 (2023) - [c9]Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Zihan Ding, Chi Jin, Mengdi Wang:
Representation Learning for Low-rank General-sum Markov Games. ICLR 2023 - [c8]Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang:
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. NeurIPS 2023 - [i8]Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang:
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. CoRR abs/2307.07055 (2023) - 2022
- [c7]Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang, Mengdi Wang:
Optimal Estimation of Policy Gradient via Double Fitted Iteration. ICML 2022: 16724-16783 - [c6]Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni, Mengdi Wang:
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory. ICML 2022: 26713-26749 - [c5]Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong, Csaba Szepesvári, Mengdi Wang:
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization. NeurIPS 2022 - [i7]Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang, Mengdi Wang:
Optimal Estimation of Off-Policy Policy Gradient via Double Fitted Iteration. CoRR abs/2202.00076 (2022) - [i6]Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni, Mengdi Wang:
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory. CoRR abs/2202.04970 (2022) - [i5]Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong
, Csaba Szepesvári, Mengdi Wang:
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization. CoRR abs/2206.02092 (2022) - [i4]Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Chi Jin, Mengdi Wang:
Representation Learning for General-sum Low-rank Markov Games. CoRR abs/2210.16976 (2022) - 2021
- [c4]Chengzhuo Ni, Anru R. Zhang, Yaqi Duan, Mengdi Wang:
Learning Good State and Action Representations via Tensor Decomposition. ISIT 2021: 1682-1687 - [c3]Junyu Zhang, Chengzhuo Ni, Zheng Yu, Csaba Szepesvári, Mengdi Wang:
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method. NeurIPS 2021: 2228-2240 - [i3]Junyu Zhang, Chengzhuo Ni, Zheng Yu, Csaba Szepesvári, Mengdi Wang:
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method. CoRR abs/2102.08607 (2021) - [i2]Chengzhuo Ni, Anru Zhang, Yaqi Duan, Mengdi Wang:
Learning Good State and Action Representations via Tensor Decomposition. CoRR abs/2105.01136 (2021)
2010 – 2019
- 2019
- [c2]Chengzhuo Ni, Lin F. Yang, Mengdi Wang:
Learning to Control in Metric Space with Optimal Regret. Allerton 2019: 726-733 - [c1]Chengzhuo Ni, Mengdi Wang:
Maximum Likelihood Tensor Decomposition of Markov Decision Process. ISIT 2019: 3062-3066 - [i1]Lin F. Yang, Chengzhuo Ni, Mengdi Wang:
Learning to Control in Metric Space with Optimal Regret. CoRR abs/1905.01576 (2019)
Coauthor Index
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