Learning-To-Measure: In-Context Active Feature Acquisition

arXiv:2510.12624v2 Announce Type: replace Abstract: Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire. In practice, AFA methods often learn from retrospective data with systematic missingness in the features and limited task-specific labels. Most prior work addresses acquisition for a single predetermined task, limiting scalability. To address this limitation, we formalize the meta-AFA problem, where the goal is to learn acquisition policies across various

Source: arXiv cs.LG — read the full report at the original publisher.

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