This paper focuses on accurate uncertainty estimation for open-set text classification (OSTC) systems, where text samples can be classified into existing classes or rejected as unknown. It adapts the Holistic Uncertainty Estimation (HolUE) method for the text domain to capture text and gallery uncertainties, and proposes a new OSTC benchmark.
A new general bias-variance decomposition for strictly proper scoring rules has finally been introduced in an AISTATS 2023 paper by Gruber & Buettner. This advancement provides practical tools for understanding ensemble models, constructing confidence regions, and improving out-of-distribution detection, addressing a long-standing gap in uncertainty estimation.
The Distill-Belief framework addresses the challenge of efficient and accurate inverse source localization and characterization (ISLC) for mobile agents by balancing correctness and efficiency. It proposes a teacher-student model, where a Bayes-correct particle filter teacher guides a compact student for fast, uncertainty-aware decision-making in real-time.