Related papers: What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization

What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization

URL: http://arxiv.org/abs/2207.05161v2
Date: Fri, 27 Oct 2023 19:09:59 GMT
Title: What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization
Authors: Hao Sun, Boris van Breugel, Jonathan Crabbe, Nabeel Seedat, Mihaela van der Schaar
Abstract summary: Uncertainty Quantification (UQ) is essential for creating trustworthy machine learning models. Recent years have seen a steep rise in UQ methods that can flag suspicious examples. We propose a framework for categorizing uncertain examples flagged by UQ methods in classification tasks.
Score: 68.15353480798244
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Uncertainty Quantification (UQ) is essential for creating trustworthy machine learning models. Recent years have seen a steep rise in UQ methods that can flag suspicious examples, however, it is often unclear what exactly these methods identify. In this work, we propose a framework for categorizing uncertain examples flagged by UQ methods in classification tasks. We introduce the confusion density matrix -- a kernel-based approximation of the misclassification density -- and use this to categorize suspicious examples identified by a given uncertainty method into three classes: out-of-distribution (OOD) examples, boundary (Bnd) examples, and examples in regions of high in-distribution misclassification (IDM). Through extensive experiments, we show that our framework provides a new and distinct perspective for assessing differences between uncertainty quantification methods, thereby forming a valuable assessment benchmark.

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