  We consider active learning with logged data, where labeled examples are
drawn conditioned on a predetermined logging policy, and the goal is to learn a
classifier on the entire population, not just conditioned on the logging
policy. Prior work addresses this problem either when only logged data is
available, or purely in a controlled random experimentation setting where the
logged data is ignored. In this work, we combine both approaches to provide an
algorithm that uses logged data to bootstrap and inform experimentation, thus
achieving the best of both worlds. Our work is inspired by a connection between
controlled random experimentation and active learning, and modifies existing
disagreement-based active learning algorithms to exploit logged data.
