  Truly intelligent systems are expected to make critical decisions with
incomplete and uncertain data. Active feature acquisition (AFA), where features
are sequentially acquired to improve the prediction, is a step towards this
goal. However, current AFA models all deal with a small set of candidate
features and have difficulty scaling to a large feature space. Moreover, they
are ignorant about the valid domains where they can predict confidently, thus
they can be vulnerable to out-of-distribution (OOD) inputs. In order to remedy
these deficiencies and bring AFA models closer to practical use, we propose
several techniques to advance the current AFA approaches. Our framework can
easily handle a large number of features using a hierarchical acquisition
policy and is more robust to OOD inputs with the help of an OOD detector for
partially observed data. Extensive experiments demonstrate the efficacy of our
framework over strong baselines.
