  Leveraging the wealth of unlabeled data produced in recent years provides
great potential for improving supervised models. When the cost of acquiring
labels is high, probabilistic active learning methods can be used to greedily
select the most informative data points to be labeled. However, for many
large-scale problems standard greedy procedures become computationally
infeasible and suffer from negligible model change. In this paper, we introduce
a novel Bayesian batch active learning approach that mitigates these issues.
Our approach is motivated by approximating the complete data posterior of the
model parameters. While naive batch construction methods result in correlated
queries, our algorithm produces diverse batches that enable efficient active
learning at scale. We derive interpretable closed-form solutions akin to
existing active learning procedures for linear models, and generalize to
arbitrary models using random projections. We demonstrate the benefits of our
approach on several large-scale regression and classification tasks.
