From Uncertainty to Failure Attribution: Self-Diagnosing Models for Failure Attribution under Distribution Shift

Distribution shift poses a significant challenge to the robustness of machine learning models, but the current solutions only aim to detect out-of-distribution (OOD) samples and predict uncertainty levels. We introduce a problem setting for failure attribution under distribution shift, which enables