Gradient and Newton boosting for classification and regression. - Fabio Sigrist (Expert Syst. Appl., 2021)

KTBoost: Combined Kernel and Tree Boosting. - Fabio Sigrist (Neural Process. Lett., 2021)

Joint variable selection of both fixed and random effects for Gaussian process-based spatially varying coefficient models. - Jakob A. Dambon; Fabio Sigrist; Reinhard Furrer (Int. J. Geogr. Inf. Sci., 2022)

Gaussian Process Boosting. - Fabio Sigrist (J. Mach. Learn. Res., 2022)

A Comparison of Machine Learning Methods for Data with High-Cardinality Categorical Variables. - Fabio Sigrist (CoRR, 2023)

Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models. - Pascal Kündig; Fabio Sigrist (CoRR, 2023)

Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities. - Fabio Sigrist; Nicola Leuenberger (Eur. J. Oper. Res., 2023)

Latent Gaussian Model Boosting. - Fabio Sigrist (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data. - Tim Gyger; Reinhard Furrer; Fabio Sigrist (CoRR, 2024)

A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios. - Pascal Kündig; Fabio Sigrist (CoRR, 2024)

Scalable Computations for Generalized Mixed Effects Models with Crossed Random Effects Using Krylov Subspace Methods. - Pascal Kündig; Fabio Sigrist (CoRR, 2025)

Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes. - Tim Gyger; Reinhard Furrer; Fabio Sigrist (CoRR, 2025)