  Active search formalizes a specialized active learning setting where the goal
is to collect members of a rare, valuable class. The state-of-the-art algorithm
approximates the optimal Bayesian policy in a budget-aware manner, and has been
shown to achieve impressive empirical performance in previous work. However,
even this approximate policy has a superlinear computational complexity with
respect to the size of the search problem, rendering its application
impractical in large spaces or in real-time systems where decisions must be
made quickly. We study the amortization of this policy by training a neural
network to learn to search. To circumvent the difficulty of learning from
scratch, we appeal to imitation learning techniques to mimic the behavior of
the expert, expensive-to-compute policy. Our policy network, trained on
synthetic data, learns a beneficial search strategy that yields nonmyopic
decisions carefully balancing exploration and exploitation. Extensive
experiments demonstrate our policy achieves competitive performance at
real-world tasks that closely approximates the expert's at a fraction of the
cost, while outperforming cheaper baselines.
