2 citations · 2 across the 1 of their papers we have counts for
4 papers
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…
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…