We review generalized additive models as a type of 'transparent'model that has recently
seen renewed interest in the deep learning community as neural additive models. We …

The main objective of this thesis is to increase trust in Machine Learning models by
developing tools capable of explaining their predictions and quantifying the associated …

Generalized additive models (GAM) have been successfully applied to high dimensional
data analysis. However, most existing methods cannot simultaneously estimate the link …

Forecasters using flexible neural networks (NN) in multi-horizon distributional regression
setups often struggle to gain detailed insights into the underlying mechanisms that lead to …

Generalized Additive Models (GAMs) are widely recognized for their ability to create fully
interpretable machine learning models for tabular data. Traditionally, training GAMs involves …

In recent years, deep neural networks have showcased their predictive power across a
variety of tasks. The transformer architecture, originally developed for natural language …

This thesis investigates the application of spline regression models to predict the Probability
of Default (PD) under varying macroeconomic conditions, exploring whether these models …