To enable valid statistical inference in prediction tasks, we show how to generate set-valued predictions with black-box models that control various notions of statistical error. Our approach guarantees that the expected loss on future test points falls below a user-specified level, for any predictive model and underlying distribution. Building on conformal prediction, we use a holdout set to...