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Reduce testing costs without compromising predictive accuracy by learning cost-optimal sequential decision policies from retrospective data, even with informative missingness.
By injecting semantic knowledge into latent space models, this work shows how to learn more robust representations from imbalanced data, a pervasive challenge in fields like healthcare.
