5 citations · 10 across the 4 of their papers we have counts for
7 papers
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression
Lucas Kook, Chris Kolb, Philipp Schiele +8
Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithm…
Estimating Conditional Distributions with Neural Networks using R package deeptrafo
Lucas Kook, Philipp FM Baumann, Oliver Dürr +2
Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classi…
Deep interpretable ensembles
Lucas Kook, Andrea Götschi, Philipp FM Baumann +2
Ensembles improve prediction performance and allow uncertainty quantification by aggregating predictions from multiple models. In deep ensembling, the individual models are usually…
Probabilistic Time Series Forecasts with Autoregressive Transformation Models
David Rügamer, Philipp F. M. Baumann, Thomas Kneib +1
Probabilistic forecasting of time series is an important matter in many applications and research fields. In order to draw conclusions from a probabilistic forecast, we must ensure…
Translational Equivariance in Kernelizable Attention
Max Horn, Kumar Shridhar, Elrich Groenewald +1
While Transformer architectures have show remarkable success, they are bound to the computation of all pairwise interactions of input element and thus suffer from limited scalabili…
Deep Conditional Transformation Models
Philipp F. M. Baumann, Torsten Hothorn, David Rügamer
Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set of features remains challenging, especially in high-dimensional settings. Conditiona…
Selective Inference for Additive and Linear Mixed Models
David Rügamer, Philipp F. M. Baumann, Sonja Greven
This work addresses the problem of conducting valid inference for additive and linear mixed models after model selection. One possible solution to overcome overconfident inference…