3 papers
cs.LG2026
Understanding Uncertainty Sampling via Equivalent Loss
Shang Liu, Xiaocheng Li
Uncertainty sampling is a prevalent active learning algorithm that queries sequentially the annotations of data samples which the current prediction model is uncertain about. Howev…
cs.AI2025
Quantile Markov Decision Process
Xiaocheng Li, Huaiyang Zhong, Margaret L. Brandeau
The goal of a traditional Markov decision process (MDP) is to maximize expected cumulative reward over a defined horizon (possibly infinite). In many applications, however, a decis…
cs.LG2025
Exploration-free Algorithms for Multi-group Mean Estimation
Ziyi Wei, Huaiyang Zhong, Xiaocheng Li
We address the problem of multi-group mean estimation, which seeks to allocate a finite sampling budget across multiple groups to obtain uniformly accurate estimates of their means…