This work develops central limit theorems for cross-validation and consistent estimators of
the asymptotic variance under weak stability conditions on the learning algorithm. Together …

Ensemble learning aggregates outputs from multiple base learners for better performance.
Bootstrap aggregating (bagging) and boosting are two popular such approaches. They are …

The accuracy and interpretability of a (non-life) insurance pricing model are essential
qualities to ensure fair and transparent premiums for policy-holders, that reflect their risk. In …

Conformal inference is a popular tool for constructing prediction intervals (PIs). Due to the
consideration of computational burden, one of the most commonly used conformal methods …

We provide a novel dimension-free uniform concentration bound for the empirical risk
function of constrained logistic regression. Our bound yields a milder sufficient condition for …

Accuracy and interpretability of a (non-life) insurance pricing model are essential qualities to
ensure fair and transparent premiums for policy-holders, that reflect their risk. In recent …

Due to its complexity, much effort has been devoted to the development of biomarkers for
prostate cancer that have acquired the utmost clinical relevance for diagnosis and grading …

Survival Analysis in Distributed and High-Dimensional Environments and Theory of Cross-Validation
Page 1 Survival Analysis in Distributed and High-Dimensional Environments and Theory of …

A plethora of high-impact applications involve predictive modeling of structured data. In
various domains, from hospital readmission prediction in the medical realm, though weather …

This thesis consists of three chapters. The first chapter extends the recent result of Athey and
Wager on the asymptotic normality of random forest estimators to a multivariate setting; in …