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arXiv · 2111.03135

Scaffolding Sets

Maya Burhanpurkar·Zhun Deng·Cynthia Dwork·Linjun Zhang
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Abstract

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.

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Maya Burhanpurkar, Zhun Deng, Cynthia Dwork, Linjun Zhang. 2021-11-04. Scaffolding Sets. https://arxiv.org/abs/2111.03135

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