  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.
