A neural network learns non-stationary anisotropic correlations from gridded CTM outputs and transfers the structure via LatticeKrig basis functions to station data for refined fine-scale NO2 predictions with uncertainty.
Journal of agricultural, biological and environmental Statistics , volume=
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Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.
citing papers explorer
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A Non-stationary, Amortized, Transfer Learning Approach for Modeling Italian Air Quality
A neural network learns non-stationary anisotropic correlations from gridded CTM outputs and transfers the structure via LatticeKrig basis functions to station data for refined fine-scale NO2 predictions with uncertainty.
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Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
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A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.