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# Two fundamental limits for uncertainty quantification in predictive inference

*Proceedings of Thirty Seventh Conference on Learning Theory*, PMLR 247:186-218, 2024.

#### Abstract

We study the statistical hardness of estimating two basic representations of uncertainty in predictive inference: prediction sets and calibration error. First, we show that conformal prediction sets cannot approach a desired weighted conformal coverage level—with respect to a family of binary witness functions with VC dimension $d$—at a minimax rate faster than $O(d^{1/2}n^{-1/2})$. We also show that the algorithm in Gibbs et al. (2023) achieves this rate and that extending our class of conformal sets beyond thresholds of non-conformity scores to include arbitrary convex sets of non-conformity scores only improves the minimax rate by a constant factor. Then, under a similar VC dimension constraint on the witness function class, we show it is not possible to estimate the weighted weak calibration error at a minimax rate faster than $O(d^{1/4}n^{-1/2})$. We show that the algorithm in Kumar et al. (2019) achieves this rate in the particular case of estimating the squared weak calibration error of a predictor that outputs $d$ distinct values.