66 citations · 132 across the 11 of their papers we have counts for
14 papers
Interpretable Neural Causal Models with TRAM-DAGs
Beate Sick, Oliver Dürr
The ultimate goal of most scientific studies is to understand the underlying causal mechanism between the involved variables. Structural causal models (SCMs) are widely used to rep…
Bayesian Semi-structured Subspace Inference
Daniel Dold, David Rügamer, Beate Sick +1
Semi-structured regression models enable the joint modeling of interpretable structured and complex unstructured feature effects. The structured model part is inspired by statistic…
Single-shot Bayesian approximation for neural networks
Kai Brach, Beate Sick, Oliver Dürr
Deep neural networks (NNs) are known for their high-prediction performances. However, NNs are prone to yield unreliable predictions when encountering completely new situations with…
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 transformation models for functional outcome prediction after acute ischemic stroke
Lisa Herzog, Lucas Kook, Andrea Götschi +6
In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semi-structured data like tabular and ima…
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…