PAC Prediction Sets for Meta-Learning

Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani

Keywords: uncertainty quantification, prediction sets, probably approximately correct, conformal prediction, meta learning, few-shot learning

TL;DR: We propose a novel algorithm to construct a prediction set for meta learning that satisfies a probably approximately correct (PAC) guarantee tailored to meta learning.

Abstract: Uncertainty quantification is a key component of machine learning models targeted at safety-critical systems such as in healthcare or autonomous vehicles. We study this problem in the context of meta learning, where the goal is to quickly adapt a predictor to new tasks. In particular, we propose a novel algorithm to construct \emph{PAC prediction sets}, which capture uncertainty via sets of labels, that can be adapted to new tasks with only a few training examples. These prediction sets satisfy an extension of the typical PAC guarantee to the meta learning setting; in particular, the PAC guarantee holds with high probability over future tasks. We demonstrate the efficacy of our approach on four datasets across three application domains: mini-ImageNet and CIFAR10-C in the visual domain, FewRel in the language domain, and the CDC Heart Dataset in the medical domain. In particular, our prediction sets satisfy the PAC guarantee while having smaller size compared to other baselines that also satisfy this guarantee.

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:2207.02440/code)