  Most prior work on active learning of classifiers has focused on sequentially
selecting one unlabeled example at a time to be labeled in order to reduce the
overall labeling effort. In many scenarios, however, it is desirable to label
an entire batch of examples at once, for example, when labels can be acquired
in parallel. This motivates us to study batch active learning, which
iteratively selects batches of $k>1$ examples to be labeled. We propose a novel
batch active learning method that leverages the availability of high-quality
and efficient sequential active-learning policies by attempting to approximate
their behavior when applied for $k$ steps. Specifically, our algorithm first
uses Monte-Carlo simulation to estimate the distribution of unlabeled examples
selected by a sequential policy over $k$ step executions. The algorithm then
attempts to select a set of $k$ examples that best matches this distribution,
leading to a combinatorial optimization problem that we term "bounded
coordinated matching". While we show this problem is NP-hard in general, we
give an efficient greedy solution, which inherits approximation bounds from
supermodular minimization theory. Our experimental results on eight benchmark
datasets show that the proposed approach is highly effective
