Understanding Uncertainty Sampling via Equivalent Loss

ArXi:2307.02719v4 Announce Type: replace Uncertainty sampling is a prevalent active learning algorithm that queries sequentially the annotations of data samples which the current prediction model is uncertain about. However, the usage of uncertainty sampling has been largely heuristic: There is no consensus on the proper definition of ``uncertainty'' for a specific task under a specific loss, nor a theoretical guarantee that prescribes a standard protocol to implement the algorithm.