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Found 8 papers, 7 papers with code
Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models
Latent Gaussian process (GP) models are flexible probabilistic non-parametric function models.
A Comparison of Machine Learning Methods for Data with High-Cardinality Categorical Variables
High-cardinality categorical variables are variables for which the number of different levels is large relative to the sample size of a data set, or in other words, there are few data points per level.
Latent Gaussian Model Boosting
Latent Gaussian models and boosting are widely used techniques in statistics and machine learning.
Joint Variable Selection of both Fixed and Random Effects for Gaussian Process-based Spatially Varying Coefficient Models
It relies on a penalized maximum likelihood estimation (PMLE) and allows variable selection both with respect to fixed effects and Gaussian process random effects.
Variable Selection
Methodology
Gaussian Process Boosting
We introduce a novel way to combine boosting with Gaussian process and mixed effects models.
KTBoost: Combined Kernel and Tree Boosting
We introduce a novel boosting algorithm called `KTBoost' which combines kernel boosting and tree boosting.
Gradient and Newton Boosting for Classification and Regression
In addition, we introduce a novel tuning parameter for tree-based Newton boosting which is interpretable and important for predictive accuracy.
Grabit: Gradient Tree Boosted Tobit Models for Default Prediction
A frequent problem in binary classification is class imbalance between a minority and a majority class such as defaults and non-defaults in default prediction.
Methodology
