4 papers
Adjustment for Confounding using Pre-Trained Representations
Rickmer Schulte, David Rügamer, Thomas Nagler
There is growing interest in extending average treatment effect (ATE) estimation to incorporate non-tabular data, such as images and text, which may act as sources of confounding.…
Vine Copulas as Differentiable Computational Graphs
Tuoyuan Cheng, Thibault Vatter, Thomas Nagler +1
Vine copulas are sophisticated models for multivariate distributions and are increasingly used in machine learning. To facilitate their integration into modern ML pipelines, we int…
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals
Marcel Arpogaus, Thomas Kneib, Thomas Nagler +1
Density regression models allow a comprehensive understanding of data by modeling the complete conditional probability distribution. While flexible estimation approaches such as no…
Constructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Study
Hannah Schulz-Kümpel, Sebastian Fischer, Roman Hornung +3
When assessing the quality of prediction models in machine learning, confidence intervals (CIs) for the generalization error, which measures predictive performance, are a crucial t…