Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

TL;DR

This paper evaluates the empirical frequentist coverage of various uncertainty quantification methods in deep learning, revealing their limitations under dataset shifts and emphasizing coverage as a key metric for real-world applications.

Contribution

It provides the first large-scale empirical assessment of coverage properties of uncertainty methods in deep learning, highlighting their shortcomings under distributional shifts.

Findings

Coverage is achieved on in-distribution data for some methods.

Coverage deteriorates significantly under out-of-distribution conditions.

Current techniques often fail to maintain reliable uncertainty estimates with dataset shift.

Abstract

Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings. Currently, the quality of a model's uncertainty is evaluated using point-prediction metrics such as negative log-likelihood or the Brier score on heldout data. In this study, we provide the first large scale evaluation of the empirical frequentist coverage properties of well known uncertainty quantification techniques on a suite of regression and classification tasks. We find that, in general, some methods do achieve desirable coverage properties on in distribution samples, but that coverage is not maintained on out-of-distribution data. Our results demonstrate the failings of current uncertainty quantification techniques as dataset shift increases and establish coverage as an important metric in developing models for real-world applications.