Offline Planning Approach Trains Feature-Acquisition Policies Under Budget Limits
A new arXiv paper introduces O-MPAC, a method that learns which feature groups to query when data acquisition is costly and the value of a query depends on later choices. The approach transfers risk-cost targets from complete training records into a single scorer that ranks sources and actions. It is designed to work offline and to respect a fixed finite-horizon acquisition budget.