David Rügamer

Automatic Componentwise Boosting: An Interpretable AutoML System

2021 · arXiv (Cornell University)

In practice, machine learning (ML) workflows require various different steps, from data preprocessing, missing value imputation, model selection, to model tuning as well as model evaluation. Many of these steps rely on human ML experts. …

Bayesian Semi-structured Subspace Inference

2024 · arXiv (Cornell University)

Semi-structured regression models enable the joint modeling of interpretable structured and complex unstructured feature effects. The structured model part is inspired by statistical models and can be used to infer the input-output relationship for features …

Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals

2025 · arXiv (Cornell University)

Density regression models allow a comprehensive understanding of data by modeling the complete conditional probability distribution. While flexible estimation approaches such as normalizing flows (NF) work particularly well in multiple dimensions, interpreting the input-output relationship …