8 citations · 11 across the 14 of their papers we have counts for
17 papers
Guiding Posterior Exploration with Optimizer-Derived Geometry
Moritz Schlager, Emanuel Sommer, Thomas Möllenhoff +1
Sampling-based methods offer a principled approach to uncertainty quantification in Bayesian neural networks. Their practical use, however, is often challenged by the computational…
On the Epistemic Uncertainty of Overparametrized Neural Networks
David Rügamer
Epistemic uncertainty is often viewed as a reducible uncertainty that vanishes with increasing data. This perspective implicitly assumes parameter identifiability and equates epist…
Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning
Emanuel Sommer, David Rügamer
The practical adoption of sampling-based inference (SAI) in Bayesian neural networks (BNNs) remains limited, partly due to persistent misconceptions about the feasibility and effic…
bde: A Python Package for Bayesian Deep Ensembles via MILE
Vyron Arvanitis, Angelos Aslanidis, Emanuel Sommer +1
bde is a user-friendly Python package for Bayesian Deep Ensembles with a particular focus on tabular data. Built on an efficient JAX implementation of the sampling-based inference…
Rethinking Intrinsic Dimension Estimation in Neural Representations
Rickmer Schulte, David Rügamer
The analysis of neural representation has become an integral part of research aiming to better understand the inner workings of neural networks. While there are many different appr…
Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles
Emanuel Sommer, Rickmer Schulte, Sarah Deubner +2
Bayesian Deep Ensembles (BDEs) represent a powerful approach for uncertainty quantification in deep learning, combining the robustness of Deep Ensembles (DEs) with flexible multi-c…
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…
Enhancing Heavy Rain Nowcasting with Multimodal Data: Integrating Radar and Satellite Observations
Rama Kassoumeh, David Rügamer, Henning Oppel
The increasing frequency of heavy rainfall events, which are a major cause of urban flooding, underscores the urgent need for accurate precipitation forecasting - particularly in u…
Adjustment for Confounding using Pre-Trained Representations
Rickmer Schulte, David Rügamer, Thomas Nagler
There is growing interest in extending average treatment effect (ATE) estimation to incorporate non-tabular data, such as images and text, which may act as sources of confounding.…
Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors
Thomas Nagler, David Rügamer
Prior-data fitted networks (PFNs) have emerged as promising foundation models for prediction from tabular datasets, achieving state-of-the-art performance on small to moderate data…
How To Make Your Cell Tracker Say "I dunno!"
Richard D. Paul, Johannes Seiffarth, David Rügamer +2
Cell tracking is a key computational task in live-cell microscopy, but fully automated analysis of high-throughput imaging requires reliable and, thus, uncertainty-aware data analy…
Additive Model Boosting: New Insights and Path(ologie)s
Rickmer Schulte, David Rügamer
Additive models (AMs) have sparked a lot of interest in machine learning recently, allowing the incorporation of interpretable structures into a wide range of model classes. Many c…
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…
Can Transformers Learn Full Bayesian Inference in Context?
Arik Reuter, Tim G. J. Rudner, Vincent Fortuin +1
Transformers have emerged as the dominant architecture in the field of deep learning, with a broad range of applications and remarkable in-context learning (ICL) capabilities. Whil…
Interpretable Machine Learning for TabPFN
David Rundel, Julius Kobialka, Constantin von Crailsheim +3
The recently developed Prior-Data Fitted Networks (PFNs) have shown very promising results for applications in low-data regimes. The TabPFN model, a special case of PFNs for tabula…
Uncertainty-aware predictive modeling for fair data-driven decisions
Patrick Kaiser, Christoph Kern, David Rügamer
Both industry and academia have made considerable progress in developing trustworthy and responsible machine learning (ML) systems. While critical concepts like fairness and explai…
Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach
Magdalena Mittermeier, Maximilian Weigert, David Rügamer
Europe was hit by several, disastrous heat and drought events in recent summers. Besides thermodynamic influences, such hot and dry extremes are driven by certain atmospheric situa…