Papers
1
Total Citations
6
H-Index
1
About
Hang Xiong is a researcher whose work centers on advancing Bayesian optimization (BO) for expensive black-box problems, with applications spanning parameter tuning, experimental design, and robotics. Their key contribution, "Bayesian Optimization using Pseudo-Points" (2020), introduces a novel framework that enhances traditional Gaussian process (GP)-based BO by leveraging pseudo-points to improve model efficiency and scalability. This approach addresses critical limitations in standard BO, such as computational bottlenecks and poor handling of non-stationary objectives, offering a more flexible and data-efficient alternative. While still early in its citation trajectory (6 citations), the work has already garnered attention for its potential to streamline optimization in resource-constrained settings. Xiong’s research bridges theoretical rigor with practical utility, positioning them as an emerging voice in the optimization community. Their focus on pseudo-point methods reflects a broader commitment to making BO more accessible for real-world challenges, from robotics control to automated machine learning. For students and researchers exploring efficient black-box optimization, Xiong’s contributions offer a promising pathway to tackle complex, high-dimensional problems with limited data.
Research Focus
Key Achievements
Top Papers
- 1Bayesian Optimization using Pseudo-Points6 citations · 2020