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This research addresses a critical bias in active feature acquisition where standard entropy rewards conflate epistemic and aleatoric uncertainty, leading to the penalization of sparsely observed features. By shifting the reward function to target posterior expected (aleatoric) entropy specifically, the authors demonstrate reduced value estimation bias and improved coverage for downstream policy selection using prior-data fitted networks. The study offers a more robust framework for sequential data acquisition in scenarios with imbalanced offline datasets.
Evaluation of Active Feature Acquisition Policies with Tabular Foundation Models
arXiv:2610.07406v1 Announce Type: new Abstract: Active feature acquisition learns policies that sequentially acquire features to maximize information about a target variable. We study how to learn and evaluate such policies from finite offline data using prior-data fitted networks (PFNs), which are off-the-shelf models that output posterior predictive distributions without task-specific training. We show that under the imbalanced coverage of offline data, using total predictive entropy as a reward creates an epistemic bias that penalizes acquiring sparsely observed features. Specifically, this reward conflates epistemic uncertainty (arising from lack of offline data) with aleatoric uncertainty (arising from uninformative features). To address this, we target the posterior expected (aleatoric) entropy instead of the total predictive entropy output by a PFN for evaluating feature acquisitions. Empirical evaluations on synthetic and real-world datasets demonstrate that our approach consistently reduces value estimation bias and yields credible intervals with strong empirical coverage, which can translate to improved downstream policy selection.
Abstract:Active feature acquisition learns policies that sequentially acquire features to maximize information about a target variable. We study how to learn and evaluate such policies from finite offline data using prior-data fitted networks (PFNs), which are off-the-shelf models that output posterior predictive distributions without task-specific training. We show that under the imbalanced coverage of offline data, using total predictive entropy as a reward creates an epistemic bias that penalizes acquiring sparsely observed features. Specifically, this reward conflates epistemic uncertainty (arising from lack of offline data) with aleatoric uncertainty (arising from uninformative features). To address this, we target the posterior expected (aleatoric) entropy instead of the total predictive entropy output by a PFN for evaluating feature acquisitions. Empirical evaluations on synthetic and real-world datasets demonstrate that our approach consistently reduces value estimation bias and yields credible intervals with strong empirical coverage, which can translate to improved downstream policy selection.
Machine Learning (cs.LG)
arXiv:2610.07406 [cs.LG]
(or arXiv:2610.07406v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.07406 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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