PAC Prediction Sets Under Covariate Shift

Understand

An important challenge facing modern machine learning is how to rigorously quantify the uncertainty of model predictions.

• Conveying uncertainty is especially important when there are changes to the underlying data distribution that might invalidate the predictive model.
• Yet, most existing uncertainty quantification algorithms break down in the presence of such shifts.
• We propose a novel approach that addresses this challenge by constructing \emph{probably approximately correct (PAC)} prediction sets in the presence of covariate shift.