Author

Linus Aronsson

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#machine learning May 2026

Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients

A continuous relaxation of the acquisition process is introduced that enables non-myopic pathwise policy gradients (NM-PPG) through the full acquisition trajectory, avoiding the high variance of standard score-function policy gradients while allowing end-to-end optimization of the acquisition policy.

Linus Aronsson, M. Chehreghani · arXiv.org · 3 citations

#artificial intelligence Preprint Sep 2026

Active Feature Acquisition With Incomplete Training Data

AFA with Incomplete Training Data is studied, showing that under missing completely at random (MCAR) data, one-step acquisition values remain unchanged, whereas multi-step values can decrease and that generative restoration is able to recover lost performance in many experiments.

Reza Rezvan, Valter Schütz, Han Wu et al. · 0 citations