7 citations · 19 across the 19 of their papers we have counts for
9 papers · 1 filter
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…
Generalizing Orthogonalization for Models with Non-Linearities
David Rügamer, Chris Kolb, Tobias Weber +2
The complexity of black-box algorithms can lead to various challenges, including the introduction of biases. These biases present immediate risks in the algorithms' application. It…
Position: Why We Must Rethink Empirical Research in Machine Learning
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger +7
We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to un…
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…
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…
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…
Deep Semi-Supervised Learning for Time Series Classification
Jann Goschenhofer, Rasmus Hvingelby, David Rügamer +3
While Semi-supervised learning has gained much attention in computer vision on image data, yet limited research exists on its applicability in the time series domain. In this work,…