3 papers
stat.ME2024
Calibrated Multivariate Regression with Localized PIT Mappings
Lucas Kock, G. S. Rodrigues, Scott A. Sisson +2
Calibration ensures that predicted uncertainties align with observed uncertainties. While there is an extensive literature on recalibration methods for univariate probabilistic for…
stat.ML2024
Positional Encoder Graph Quantile Neural Networks for Geographic Data
William E. R. de Amorim, Scott A. Sisson, T. Rodrigues +2
Positional Encoder Graph Neural Networks (PE-GNNs) are among the most effective models for learning from continuous spatial data. However, their predictive distributions are often…
stat.ME2024
Model-Free Local Recalibration of Neural Networks
R. Torres, D. J. Nott, S. A. Sisson +3
Artificial neural networks (ANNs) are highly flexible predictive models. However, reliably quantifying uncertainty for their predictions is a continuing challenge. There has been m…