Active, anytime-valid risk controlling prediction sets

Rigorously establishing the safety of black-box machine learning models with respect to critical risk measures is important for providing guarantees about the behavior of the model.Recently, a notion of a risk controlling prediction set (RCPS) has been introduced by Bates et. Further, we propose a framework for constructing RCPSes for active labeling, i.e., allowing one to use a labeling policy that chooses whether to query the true label for each received data point, and ensures the expected proportion data points whose labels are queried are below a predetermined label budget. We also describe how to use predictors (e.g., the machine learning model we are providing risk control guarantees for) to further improve the utility of our RCPSes by estimating the expected risk conditioned on the covariates.We characterize the optimal choices of label policy under a fixed label budget, and predictor, and show a regret result that relates the estimation error of the optimal labeling policy and predictor to the wealth process that underlies our RCPSes.Lastly, we present practical ways of formulating label policies and we empirically show that our label policies use fewer labels to reach higher utility than naive baseline labeling strategies on both simulations and real data.