  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?
