3 papers
cs.LG2026
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…
cs.LG2026
On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors
Julius Kobialka, Emanuel Sommer, Chris Kolb +3
Bayesian neural network (BNN) posteriors are often considered impractical for inference, as symmetries fragment them, non-identifiabilities inflate dimensionality, and weight-space…
cs.LG2025
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…