17 citations · 17 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…