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stat.ML2025
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.ME2025
Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects
Pascal Kündig, Fabio Sigrist
Mixed-effects models are widely used to model data with complex grouping structures and high-cardinality categorical predictor variables. However, for high-dimensional crossed rand…
stat.CO2025
An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial data
Filippo Rambelli, Fabio Sigrist
Gaussian processes (GPs) are flexible, probabilistic, nonparametric models widely used in fields such as spatial statistics and machine learning. A drawback of Gaussian processes i…