Author

Emanuel Sommer

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#machine learning Preprint Oct 2026

How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?

Prior-data fitted networks (PFNs) are trained to approximate the Bayesian posterior predictive distribution (PPD) of a synthetic prior, yet results about the approximation quality are surprisingly thin. Taking an empirical risk minimization perspective, we decompose the deployment error of PFNs into prior, context, and...

M. Hulskamp, Julius Kobialka, Emanuel Sommer et al. · 0 citations

Jul 2026

Guiding Posterior Exploration with Optimizer-Derived Geometry

This paper demonstrates that curvature estimates computed during the warmstart as a byproduct in adaptive optimizers such as AdamW can inform the sampling phase at negligible additional cost and consistently maintains or improves predictive performance and uncertainty quantification without any additional computational...

Moritz Schlager, Emanuel Sommer, Thomas Möllenhoff et al. · arXiv.org · 0 citations