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arXiv Machine Learning By Linus Aronsson, Morteza Haghir Chehreghani

Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients

Read the original on arXiv Machine Learning →

The paper introduces Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients (NM-PPG), a method that relaxes the feature acquisition process to allow continuous, low‑variance policy gradients over the entire acquisition trajectory. It incorporates a straight‑through rollout that mimics discrete acquisitions during inference while enabling end‑to‑end training, and provides an average‑case upper bound on gradient variance to guide temperature sharpening. Experiments on synthetic and real datasets show that NM-PPG outperforms existing active feature acquisition baselines.

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