We examine a simple stochastic strategy for adapting well-known single-point acquisition functions to allow batch active learning. Unlike acquiring the top-K points from the pool set, score- or rank-based sampling takes into account that acquisition scores change as new data are acquired. This simple strategy for adapting standard single-sample acquisition strategies can even perform just as well as compute-intensive state-of-the-art batch acquisition functions, like BatchBALD or BADGE, while using orders of magnitude less compute. In addition to providing a practical option for machine learning practitioners, the surprising success of the proposed method in a wide range of experimental settings raises a difficult question for the field: when are these expensive batch acquisition methods pulling their weight?

翻译：本文研究了一种简单的随机策略，用于将常见的单点采集函数适配为支持批次主动学习。与从池中选取前K个点不同，基于分数或排名的采样考虑了采集分数随新数据获取而变化的特性。这种简单的标准单样本采集策略适配方法，在计算量低数个数量级的情况下，甚至能达到与计算密集型的最先进批次采集函数（如BatchBALD或BADGE）相当的性能。除了为机器学习从业者提供实用选择外，该方法在广泛实验设置中的惊人成功还引发了一个领域内难题：这些昂贵的批次采集方法何时才能真正发挥其价值？