4 papers
On Calibration in Multi-Distribution Learning
Rajeev Verma, Volker Fischer, Eric Nalisnick
Modern challenges of robustness, fairness, and decision-making in machine learning have led to the formulation of multi-distribution learning (MDL) frameworks in which a predictor…
Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks?
Urja Khurana, Eric Nalisnick, Antske Fokkens +1
Subjective tasks in NLP have been mostly relegated to objective standards, where the gold label is decided by taking the majority vote. This obfuscates annotator disagreement and t…
Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data
Nils Lehmann, Nina Maria Gottschling, Stefan Depeweg +1
Deep neural networks (DNNs) have been successfully applied to earth observation (EO) data and opened new research avenues. Despite the theoretical and practical advances of these t…
Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions
Urja Khurana, Ivar Vermeulen, Eric Nalisnick +2
\textbf{Offensive Content Warning}: This paper contains offensive language only for providing examples that clarify this research and do not reflect the authors' opinions. Please b…