  Many real-world situations allow for the acquisition of additional relevant
information when making an assessment with limited or uncertain data. However,
traditional ML approaches either require all features to be acquired beforehand
or regard part of them as missing data that cannot be acquired. In this work,
we consider models that perform active feature acquisition (AFA) and query the
environment for unobserved features to improve the prediction assessments at
evaluation time. Our work reformulates the Markov decision process (MDP) that
underlies the AFA problem as a generative modeling task and optimizes a policy
via a novel model-based approach. We propose learning a generative surrogate
model (GSM) that captures the dependencies among input features to assess
potential information gain from acquisitions. The GSM is leveraged to provide
intermediate rewards and auxiliary information to aid the agent navigate a
complicated high-dimensional action space and sparse rewards. Furthermore, we
extend AFA in a task we coin active instance recognition (AIR) for the
unsupervised case where the target variables are the unobserved features
themselves and the goal is to collect information for a particular instance in
a cost-efficient way. Empirical results demonstrate that our approach achieves
considerably better performance than previous state of the art methods on both
supervised and unsupervised tasks.
