Towards Robust Active Feature Acquisition

TL;DR

This paper introduces an advanced active feature acquisition framework that scales to large feature spaces and enhances robustness to out-of-distribution inputs, making AFA more practical for real-world applications.

Contribution

It proposes a hierarchical acquisition policy for large feature sets and incorporates an OOD detector to improve robustness, addressing key limitations of existing AFA models.

Findings

Framework outperforms strong baselines in experiments.

Handles large feature spaces efficiently.

Improves robustness to out-of-distribution inputs.

Abstract

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.