66 citations · 132 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
Bernstein Flows for Flexible Posteriors in Variational Bayes
Oliver Dürr, Stephan Hörling, Daniel Dold +2
Variational inference (VI) is a technique to approximate difficult to compute posteriors by optimization. In contrast to MCMC, VI scales to many observations. In the case of comple…
Transformation Models for Flexible Posteriors in Variational Bayes
Sefan Hörtling, Daniel Dold, Oliver Dürr +1
The main challenge in Bayesian models is to determine the posterior for the model parameters. Already, in models with only one or few parameters, the analytical posterior can only…
Deep and interpretable regression models for ordinal outcomes
Lucas Kook, Lisa Herzog, Torsten Hothorn +2
Outcomes with a natural order commonly occur in prediction tasks and often the available input data are a mixture of complex data like images and tabular predictors. Deep Learning…
Deep transformation models: Tackling complex regression problems with neural network based transformation models
Beate Sick, Torsten Hothorn, Oliver Dürr
We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks, it is pr…