From the 1 of 4 linked papers with an AI index.
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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…
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla +22
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language dat…
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression
Lucas Kook, Chris Kolb, Philipp Schiele +8
Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithm…
Post-hoc Orthogonalization for Mitigation of Protected Feature Bias in CXR Embeddings
Tobias Weber, Michael Ingrisch, Bernd Bischl +1
Purpose: To analyze and remove protected feature effects in chest radiograph embeddings of deep learning models. Methods: An orthogonalization is utilized to remove the influence o…