13 citations · 26 across the 5 of their papers we have counts for
5 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…
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
Zheng Yu, Yikuan Li, Joseph Kim +3
Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test pane…
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data
Minshuo Chen, Kaixuan Huang, Tuo Zhao +1
Diffusion models achieve state-of-the-art performance in various generation tasks. However, their theoretical foundations fall far behind. This paper studies score approximation, e…
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
Kaixuan Huang, Yu Wu, Xuezhou Zhang +4
Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network model by selecting a few seed nodes. Recent studies follow…