  Given a set of observations, feature acquisition is about finding the subset
of unobserved features which would enhance accuracy. Such problems have been
explored in a sequential setting in prior work. Here, the model receives
feedback from every new feature acquired and chooses to explore more features
or to predict. However, sequential acquisition is not feasible in some settings
where time is of the essence. We consider the problem of feature acquisition in
batch, where the subset of features to be queried in batch is chosen based on
the currently observed features, and then acquired as a batch, followed by
prediction. We solve this problem using several technical innovations. First,
we use a feature generator to draw a subset of the synthetic features for some
examples, which reduces the cost of oracle queries. Second, to make the feature
acquisition problem tractable for the large heterogeneous observed features, we
partition the data into buckets, by borrowing tools from locality sensitive
hashing and then train a mixture of experts model. Third, we design a tractable
lower bound of the original objective. We use a greedy algorithm combined with
model training to solve the underlying problem. Experiments with four datasets
show that our approach outperforms these methods in terms of trade-off between
accuracy and feature acquisition cost.
