From the 1 of 12 linked papers with an AI index.
12 papers
Selecting Hyperparameters for Tree-Boosting
Floris Jan Koster, Fabio Sigrist
The paper empirically evaluates several hyperparameter optimization methods for tree-boosting on many regression and classification data sets and finds that SMAC consistently outpe…
Spectrally Deconfounded Gradient Boosting
Andrea Nava, Peter Bühlmann, Fabio Sigrist
Flexible machine-learning methods can be sensitive to hidden confounding: they may learn associations induced by unobserved confounders rather than stable signals. Spectral deconfo…
A Censored Transformed Model for Proportional Outcomes with Boundary Mass and an Application to Loss Given Default Modeling
Yuan Christopher Qiang, Fabio Sigrist
We introduce the zero-one censored transformed normal (ZOC-TN) model for proportional responses with potential probability mass at the boundaries 0 and 1. The model combines a cens…
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…
Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
Andrea Nava, Fabio Sigrist
Laplace approximations are a standard tool for computationally efficient inference in latent Gaussian models, but they fail for quantile regression with the asymmetric Laplace like…
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…