AI RESEARCH
Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients
arXiv CS.LG
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ArXi:2605.05511v1 Announce Type: new Active feature acquisition (AFA) considers prediction problems in which features are costly to obtain and the learner adaptively decides which feature values to acquire for each instance and when to stop and predict. AFA can be formulated as a partially observable Marko decision process (POMDP), which naturally admits a sequential decision-making perspective. In this paper, we present non-myopic pathwise policy gradients (NM-PPG), a new AFA method built around this formulation. We.