  This note explores probabilistic sampling weighted by uncertainty in active
learning. This method has been previously used and authors have tangentially
remarked on its efficacy. The scheme has several benefits: (1) it is
computationally cheap, (2) it can be implemented in a single-pass streaming
fashion which is a benefit when deployed in real-world systems where different
subsystems perform the suggestion scoring and extraction of user feedback, and
(3) it is easily parameterizable. In this paper, we show on publicly available
datasets that using probabilistic weighting is often beneficial and strikes a
good compromise between exploration and representation especially when the
starting set of labelled points is biased.
