Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information

MCML Authors

Vincent Fortuin

Dr.

Collaborating PI

Bayesian Deep Learning

Abstract

Knowing which features of a multivariate time series to measure and when is a key task in medicine, wearables, and robotics. Better acquisition policies can reduce costs while maintaining or even improving the performance of downstream predictors. Inspired by the maximization of conditional mutual information, we propose an approach to train acquirers end-to-end using only the downstream loss. We show that our method outperforms random acquisition policy, matches a model with an unrestrained budget, but does not yet overtake a static acquisition strategy. We highlight the assumptions and outline avenues for future work.

inproceedings SMR+24

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SPIGM @ICML 2024

Workshop on Structured Probabilistic Inference & Generative Modeling at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024.

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Authors

F. Sergeev • P. Malsot • G. Rätsch • V. Fortuin

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Links

URL

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Research Area

 A1 | Statistical Foundations & Explainability