While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making. Deploying systems consequential settings also requires calibrating and communicating uncertainty predictions. To convey instance-wise tasks, we show how to generate set-valued predictions from a black-box predictor that controls expected loss on f...