3 papers
stat.ML2026
Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes
Tim Gyger, Reinhard Furrer, Fabio Sigrist
Gaussian processes are flexible, probabilistic, non-parametric models widely used in machine learning and statistics. However, their scalability to large data sets is limited by co…
stat.AP2026
Scalable non-separable spatio-temporal Gaussian process models for large-scale short-term weather prediction
Tim Gyger, Reinhard Furrer, Fabio Sigrist
Monitoring daily weather fields is critical for climate science, agriculture, and environmental planning, yet fully probabilistic spatio-temporal models become computationally proh…
stat.ME2026
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
Tim Gyger, Reinhard Furrer, Fabio Sigrist
Gaussian processes are flexible probabilistic regression models which are widely used in statistics and machine learning. However, a drawback is their limited scalability to large…