18 citations · 45 across the 9 of their papers we have counts for
9 papers
Diffusion Model for Data-Driven Black-Box Optimization
Zihao Li, Hui Yuan, Kaixuan Huang +4
Generative AI has redefined artificial intelligence, enabling the creation of innovative content and customized solutions that drive business practices into a new era of efficiency…
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
Hui Yuan, Kaixuan Huang, Chengzhuo Ni +2
We explore the methodology and theory of reward-directed generation via conditional diffusion models. Directed generation aims to generate samples with desired properties as measur…
Representation Learning for General-sum Low-rank Markov Games
Chengzhuo Ni, Yuda Song, Xuezhou Zhang +2
We study multi-agent general-sum Markov games with nonlinear function approximation. We focus on low-rank Markov games whose transition matrix admits a hidden low-rank structure on…
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization
Hui Yuan, Chengzhuo Ni, Huazheng Wang +4
Directed Evolution (DE), a landmark wet-lab method originated in 1960s, enables discovery of novel protein designs via evolving a population of candidate sequences. Recent advances…
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory
Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni +1
Off-Policy Evaluation (OPE) serves as one of the cornerstones in Reinforcement Learning (RL). Fitted Q Evaluation (FQE) with various function approximators, especially deep neural…
Optimal Estimation of Off-Policy Policy Gradient via Double Fitted Iteration
Chengzhuo Ni, Ruiqi Zhang, Xiang Ji +2
Policy gradient (PG) estimation becomes a challenge when we are not allowed to sample with the target policy but only have access to a dataset generated by some unknown behavior po…
Learning Good State and Action Representations via Tensor Decomposition
Chengzhuo Ni, Yaqi Duan, Munther Dahleh +2
The transition kernel of a continuous-state-action Markov decision process (MDP) admits a natural tensor structure. This paper proposes a tensor-inspired unsupervised learning meth…
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
Junyu Zhang, Chengzhuo Ni, Zheng Yu +2
Policy gradient (PG) gives rise to a rich class of reinforcement learning (RL) methods. Recently, there has been an emerging trend to accelerate the existing PG methods such as REI…
Learning to Control in Metric Space with Optimal Regret
Lin F. Yang, Chengzhuo Ni, Mengdi Wang
We study online reinforcement learning for finite-horizon deterministic control systems with {\it arbitrary} state and action spaces. Suppose that the transition dynamics and rewar…