Title: Latent Gaussian Model Boosting

Abstract: Latent Gaussian models and boosting are widely used techniques in statistics and machine learning. Latent Gaussian models, such as Gaussian processes and grouped random-effects models, are flexible prior models that allow for making probabilistic predictions. However, existing latent Gaussian models usually assume either a zero or a linear prior mean function which can be an unrealistic assumption. Tree-boosting achieves excellent predictive accuracy on many data sets, but potential drawbacks are that it assumes conditional independence of samples, produces discontinuous predictions for, e.g., spatial data, and it can have difficulty with high-cardinality categorical variables. We introduce a novel approach that combines boosting and latent Gaussian models to remedy the above-mentioned drawbacks and to leverage the advantages of both techniques.

References
- Sigrist F. "Gaussian Process Boosting". Journal of Machine Learning Research (2022)
- Sigrist F. "Latent Gaussian Model Boosting". IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)

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