29 citations · 55 across the 10 of their papers we have counts for
13 papers
On the Construction and Implications of Low-Loss Valleys in LoRA-based Bayesian Inference
Daniel Dold, Emanuel Sommer, Julius Kobialka +2
While parameter-efficient fine-tuning methods like low-rank adaptation (LoRA) are standard for large language models, principled estimation of epistemic uncertainty remains challen…
AutoStan: Autonomous Bayesian Model Improvement via Predictive Feedback
Oliver Dürr
We present AutoStan, a framework in which a command-line interface (CLI) coding agent autonomously builds and iteratively improves Bayesian models written in Stan. The agent operat…
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…
Paths and Ambient Spaces in Neural Loss Landscapes
Daniel Dold, Julius Kobialka, Nicolai Palm +3
Understanding the structure of neural network loss surfaces, particularly the emergence of low-loss tunnels, is critical for advancing neural network theory and practice. In this p…
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…
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…
Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
Marcel Arpogaus, Marcus Voss, Beate Sick +2
The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high flu…
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…
Single Shot MC Dropout Approximation
Kai Brach, Beate Sick, Oliver Dürr
Deep neural networks (DNNs) are known for their high prediction performance, especially in perceptual tasks such as object recognition or autonomous driving. Still, DNNs are prone…
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…