Chengzhuo Ni

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 19

Top Topics

Function Approximators

Reinforcement Learning

Policy Gradient

Top Venues

Publications

Zihao Li, Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Yinyu Ye, Minshuo Chen, Mengdi Wang
Diffusion Model for Data-Driven Black-Box Optimization. CoRR (2024)
Chengzhuo Ni, Yaqi Duan, Munther Dahleh, Mengdi Wang, Anru R. Zhang
Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition. J. Mach. Learn. Res. 24 (2023)
Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. NeurIPS (2023)
Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Zihan Ding, Chi Jin, Mengdi Wang
Representation Learning for Low-rank General-sum Markov Games. ICLR (2023)
Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. CoRR (2023)
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 (2022)
Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang, Mengdi Wang
Optimal Estimation of Off-Policy Policy Gradient via Double Fitted Iteration. CoRR (2022)
Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni, Mengdi Wang
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory. CoRR (2022)
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)
Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni, Mengdi Wang
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory. ICML (2022)
Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Chi Jin, Mengdi Wang
Representation Learning for General-sum Low-rank Markov Games. CoRR (2022)
Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang, Mengdi Wang
Optimal Estimation of Policy Gradient via Double Fitted Iteration. ICML (2022)
Junyu Zhang, Chengzhuo Ni, Zheng Yu, Csaba Szepesvári, Mengdi Wang
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method. NeurIPS (2021)
Chengzhuo Ni, Anru R. Zhang, Yaqi Duan, Mengdi Wang
Learning Good State and Action Representations via Tensor Decomposition. ISIT (2021)
Chengzhuo Ni, Anru Zhang, Yaqi Duan, Mengdi Wang
Learning Good State and Action Representations via Tensor Decomposition. CoRR (2021)
Junyu Zhang, Chengzhuo Ni, Zheng Yu, Csaba Szepesvári, Mengdi Wang
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method. CoRR (2021)
Chengzhuo Ni, Lin F. Yang, Mengdi Wang
Learning to Control in Metric Space with Optimal Regret. Allerton (2019)
Chengzhuo Ni, Mengdi Wang
Maximum Likelihood Tensor Decomposition of Markov Decision Process. ISIT (2019)
Lin F. Yang, Chengzhuo Ni, Mengdi Wang
Learning to Control in Metric Space with Optimal Regret. CoRR (2019)