Quantifying Epistemic Uncertainty in Deep Learning
This work addresses the reliability and robustness of machine learning by offering a novel framework for uncertainty quantification, which is foundational for improving model trustworthiness in applications like autonomous systems or healthcare.
The paper tackles the problem of quantifying epistemic uncertainty in deep learning by proposing a theoretical framework that dissects it into procedural and data variability, and introduces two estimation approaches based on influence functions and batching, with experimental evaluations supporting the theory and providing guidance on modeling and data collection.
Uncertainty quantification is at the core of the reliability and robustness of machine learning. In this paper, we provide a theoretical framework to dissect the uncertainty, especially the \textit{epistemic} component, in deep learning into \textit{procedural variability} (from the training procedure) and \textit{data variability} (from the training data), which is the first such attempt in the literature to our best knowledge. We then propose two approaches to estimate these uncertainties, one based on influence function and one on batching. We demonstrate how our approaches overcome the computational difficulties in applying classical statistical methods. Experimental evaluations on multiple problem settings corroborate our theory and illustrate how our framework and estimation can provide direct guidance on modeling and data collection efforts.