  Annotating the right set of data amongst all available data points is a key
challenge in many machine learning applications. Batch active learning is a
popular approach to address this, in which batches of unlabeled data points are
selected for annotation, while an underlying learning algorithm gets
subsequently updated. Increasingly larger batches are particularly appealing in
settings where data can be annotated in parallel, and model training is
computationally expensive. A key challenge here is scale - typical active
learning methods rely on diversity techniques, which select a diverse set of
data points to annotate, from an unlabeled pool. In this work, we introduce
Active Data Shapley (ADS) -- a filtering layer for batch active learning that
significantly increases the efficiency of active learning by pre-selecting,
using a linear time computation, the highest-value points from an unlabeled
dataset. Using the notion of the Shapley value of data, our method estimates
the value of unlabeled data points with regards to the prediction task at hand.
We show that ADS is particularly effective when the pool of unlabeled data
exhibits real-world caveats: noise, heterogeneity, and domain shift. We run
experiments demonstrating that when ADS is used to pre-select the
highest-ranking portion of an unlabeled dataset, the efficiency of
state-of-the-art batch active learning methods increases by an average factor
of 6x, while preserving performance effectiveness.
