Dynamic feature selection, where we sequentially query features to make
accurate predictions with a minimal budget, is a promising paradigm to reduce
feature acquisition costs and provide transparency into the prediction process.
The problem is challenging, however, as it requires both making predictions
with arbitrary feature sets and learning a policy to identify the most valuable
selections. Here, we take an information-theoretic perspective and prioritize
features based on their mutual information with the response variable. The main
challenge is learning this selection policy, and we design a straightforward
new modeling approach that estimates the mutual information in a discriminative
rather than generative fashion. Building on our learning approach, we introduce
several further improvements: allowing variable feature budgets across samples,
enabling non-uniform costs between features, incorporating prior information,
and exploring modern architectures to handle partial input information. We find
that our method provides consistent gains over recent state-of-the-art methods
across a variety of datasets.

本文介绍了一种基于信息论的动态特征选择方法，通过学习选择策略实现特征效益的高效获取。与现有最先进方法相比，该方法在各种数据集上都提供了一致的收益。