3 papers
stat.AP2026
A Non-stationary, Amortized, Transfer Learning Approach for Modeling Italian Air Quality
Alessandro Fusta Moro, Antony Sikorski, Daniel McKenzie +2
Air quality monitoring in Italy relies on sparse, irregular, ground-based stations that provide high-quality but incomplete measurements of pollution. Chemical transport models (CT…
stat.ML2026
LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data
Antony Sikorski, Michael Ivanitskiy, Nathan Lenssen +2
In many applications, we wish to fit a parametric statistical model to a small ensemble of spatially distributed random variables ('fields'). However, parameter inference using max…
stat.CO2024
Normalizing Basis Functions: Approximate Stationary Models for Large Spatial Data
Antony Sikorski, Daniel McKenzie, Douglas Nychka
In geostatistics, traditional spatial models often rely on the Gaussian Process (GP) to fit stationary covariances to data. It is well known that this approach becomes computationa…