4 papers
Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability
Henri Funk, Cornelia Gruber, Göran Kauermann +2
Predicting drought risk is essential for anticipating impacts on water resources, agriculture, ecosystems, and climate adaptation planning. Yet drought forecasts remain uncertain b…
Capturing Aleatoric Uncertainty in Climate Models
Cornelia Gruber, Henri Funk, Magdalena Mittermeier +2
Internal climate variability arises from the climate system's inherently chaotic dynamics. Quantifying it is essential for climate science, as it enables risk-based decision-making…
Revisiting Active Learning under (Human) Label Variation
Cornelia Gruber, Helen Alber, Bernd Bischl +3
Access to high-quality labeled data remains a limiting factor in applied supervised learning. While label variation (LV), i.e., differing labels for the same instance, is common, e…
Sources of Uncertainty in Supervised Machine Learning -- A Statisticians' View
Cornelia Gruber, Patrick Oliver Schenk, Malte Schierholz +2
Supervised machine learning and predictive models have achieved an impressive standard today, enabling us to answer questions that were inconceivable a few years ago. Besides these…