  Data collection and labeling is one of the main challenges in employing
machine learning algorithms in a variety of real-world applications with
limited data. While active learning methods attempt to tackle this issue by
labeling only the data samples that give high information, they generally
suffer from large computational costs and are impractical in settings where
data can be collected in parallel. Batch active learning methods attempt to
overcome this computational burden by querying batches of samples at a time. To
avoid redundancy between samples, previous works rely on some ad hoc
combination of sample quality and diversity. In this paper, we present a new
principled batch active learning method using Determinantal Point Processes, a
repulsive point process that enables generating diverse batches of samples. We
develop tractable algorithms to approximate the mode of a DPP distribution, and
provide theoretical guarantees on the degree of approximation. We further
demonstrate that an iterative greedy method for DPP maximization, which has
lower computational costs but worse theoretical guarantees, still gives
competitive results for batch active learning. Our experiments show the value
of our methods on several datasets against state-of-the-art baselines.
