8 citations · 12 across the 8 of their papers we have counts for
8 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…
Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors
Thomas Nagler, David Rügamer
Prior-data fitted networks (PFNs) have emerged as promising foundation models for prediction from tabular datasets, achieving state-of-the-art performance on small to moderate data…
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…
Generalizing Orthogonalization for Models with Non-Linearities
David Rügamer, Chris Kolb, Tobias Weber +2
The complexity of black-box algorithms can lead to various challenges, including the introduction of biases. These biases present immediate risks in the algorithms' application. It…
Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization
Thomas Nagler, Lennart Schneider, Bernd Bischl +1
Hyperparameter optimization is crucial for obtaining peak performance of machine learning models. The standard protocol evaluates various hyperparameter configurations using a resa…
Interpretable Machine Learning for TabPFN
David Rundel, Julius Kobialka, Constantin von Crailsheim +3
The recently developed Prior-Data Fitted Networks (PFNs) have shown very promising results for applications in low-data regimes. The TabPFN model, a special case of PFNs for tabula…