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

Autor:	Sergeev, Fedor, Malsot, Paola, Rätsch, Gunnar, Fortuin, Vincent
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Presented at the ICML 2024 Next Generation of Sequence Modeling Architectures (NGSM) Workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.13429 Zobrazit plný text záznamu View this record from Arxiv