  Model selection is treated as a standard performance boosting step in many
machine learning applications. Once all other properties of a learning problem
are fixed, the model is selected by grid search on a held-out validation set.
This is strictly inapplicable to active learning. Within the standardized
workflow, the acquisition function is chosen among available heuristics a
priori, and its success is observed only after the labeling budget is already
exhausted. More importantly, none of the earlier studies report a unique
consistently successful acquisition heuristic to the extent to stand out as the
unique best choice. We present a method to break this vicious circle by
defining the acquisition function as a learning predictor and training it by
reinforcement feedback collected from each labeling round. As active learning
is a scarce data regime, we bootstrap from a well-known heuristic that filters
the bulk of data points on which all heuristics would agree, and learn a policy
to warp the top portion of this ranking in the most beneficial way for the
character of a specific data distribution. Our system consists of a Bayesian
neural net, the predictor, a bootstrap acquisition function, a probabilistic
state definition, and another Bayesian policy network that can effectively
incorporate this input distribution. We observe on three benchmark data sets
that our method always manages to either invent a new superior acquisition
function or to adapt itself to the a priori unknown best performing heuristic
for each specific data set.
