most citedUncertainty quantification for probabilistic machine learning in earth observation using conformal prediction

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

stat.ME2026

Joint Spatial and Temporal Generalized Dissimilarity Mixed Modeling (stGDMM) for Beta Diversity

Philip A. White, Henry A. Frye, Jasper A. Slingsby +3

Generalized dissimilarity models (GDMs) have emerged as a valuable tool for formal statistical analysis of biodiversity. In particular, beta diversity, measured using dissimilarity…

cs.LG2024★ 3 cited

Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction

Geethen Singh, Glenn Moncrieff, Zander Venter +3

Unreliable predictions can occur when using artificial intelligence (AI) systems with negative consequences for downstream applications, particularly when employed for decision-mak…