From the 1 of 4 linked papers with an AI index.
4 papers
Higher-Order Hit-&-Run Samplers for Linearly Constrained Densities
Richard D. Paul, Anton Stratmann, Johann F. Jadebeck +4
The paper introduces a new MCMC algorithm that combines higher‑order information (gradients and curvature of the log‑density) with Hit‑and‑Run proposals to efficiently sample distr…
Position: The Future of Bayesian Prediction Is Prior-Fitted
Samuel Müller, Arik Reuter, Noah Hollmann +2
Training neural networks on randomly generated artificial datasets yields Bayesian models that capture the prior defined by the dataset-generating distribution. Prior-data Fitted N…
Calibrating LLMs with Information-Theoretic Evidential Deep Learning
Yawei Li, David Rügamer, Bernd Bischl +1
Fine-tuned large language models (LLMs) often exhibit overconfidence, particularly when trained on small datasets, resulting in poor calibration and inaccurate uncertainty estimate…
On Training Survival Models with Scoring Rules
Philipp Kopper, David Rügamer, Raphael Sonabend +2
Scoring rules are an established way of comparing predictive performances across model classes. In the context of survival analysis, they require adaptation in order to accommodate…