2 papers
stat.ML2025
Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?
Shuangning Li, Chonghuan Wang, Jingyan Wang
We study A/B experiments that are designed to compare the performance of two recommendation algorithms. Prior work has observed that the stable unit treatment value assumption (SUT…
stat.ML2023
Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
Austin Xu, Andrew D. McRae, Jingyan Wang +2
We introduce a new type of query mechanism for collecting human feedback, called the perceptual adjustment query ( PAQ). Being both informative and cognitively lightweight, the PAQ…