Stochastic Encodings for Active Feature Acquisition

Alexander Luke Ian Norcliffe, Changhee Lee, Fergus Imrie, Mihaela Van Der Schaar, Pietro Lio
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:46784-46814, 2025.

Abstract

Active Feature Acquisition is an instance-wise, sequential decision making problem. The aim is to dynamically select which feature to measure based on current observations, independently for each test instance. Common approaches either use Reinforcement Learning, which experiences training difficulties, or greedily maximize the conditional mutual information of the label and unobserved features, which makes myopic acquisitions. To address these shortcomings, we introduce a latent variable model, trained in a supervised manner. Acquisitions are made by reasoning about the features across many possible unobserved realizations in a stochastic latent space. Extensive evaluation on a large range of synthetic and real datasets demonstrates that our approach reliably outperforms a diverse set of baselines.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-norcliffe25a, title = {Stochastic Encodings for Active Feature Acquisition}, author = {Norcliffe, Alexander Luke Ian and Lee, Changhee and Imrie, Fergus and Van Der Schaar, Mihaela and Lio, Pietro}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {46784--46814}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/norcliffe25a/norcliffe25a.pdf}, url = {https://proceedings.mlr.press/v267/norcliffe25a.html}, abstract = {Active Feature Acquisition is an instance-wise, sequential decision making problem. The aim is to dynamically select which feature to measure based on current observations, independently for each test instance. Common approaches either use Reinforcement Learning, which experiences training difficulties, or greedily maximize the conditional mutual information of the label and unobserved features, which makes myopic acquisitions. To address these shortcomings, we introduce a latent variable model, trained in a supervised manner. Acquisitions are made by reasoning about the features across many possible unobserved realizations in a stochastic latent space. Extensive evaluation on a large range of synthetic and real datasets demonstrates that our approach reliably outperforms a diverse set of baselines.} }
Endnote
%0 Conference Paper %T Stochastic Encodings for Active Feature Acquisition %A Alexander Luke Ian Norcliffe %A Changhee Lee %A Fergus Imrie %A Mihaela Van Der Schaar %A Pietro Lio %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-norcliffe25a %I PMLR %P 46784--46814 %U https://proceedings.mlr.press/v267/norcliffe25a.html %V 267 %X Active Feature Acquisition is an instance-wise, sequential decision making problem. The aim is to dynamically select which feature to measure based on current observations, independently for each test instance. Common approaches either use Reinforcement Learning, which experiences training difficulties, or greedily maximize the conditional mutual information of the label and unobserved features, which makes myopic acquisitions. To address these shortcomings, we introduce a latent variable model, trained in a supervised manner. Acquisitions are made by reasoning about the features across many possible unobserved realizations in a stochastic latent space. Extensive evaluation on a large range of synthetic and real datasets demonstrates that our approach reliably outperforms a diverse set of baselines.
APA
Norcliffe, A.L.I., Lee, C., Imrie, F., Van Der Schaar, M. & Lio, P.. (2025). Stochastic Encodings for Active Feature Acquisition. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:46784-46814 Available from https://proceedings.mlr.press/v267/norcliffe25a.html.

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