Scaffolding Sets

Understand

Predictors map individual instances in a population to the interval $[0,1]$.

• For a collection $\mathcal C$ of subsets of a population, a predictor is multi-calibrated with respect to $\mathcal C$ if it is simultaneously calibrated on each set in $\mathcal C$.
• We initiate the study of the construction of scaffolding sets, a small collection $\mathcal S$ of sets with the property that multi-calibration with respect to $\mathcal S$ ensures correctness, and not just calibration, of the predictor.
• Our approach is inspired by the folk wisdom that the intermediate layers of a neural net learn a highly structured and useful data representation.