2 citations · 3 across the 9 of their papers we have counts for
9 papers
Eigenvalue Calibration for Semantic Embeddings of Large Language Models
Sebastian G. Gruber, Nassim Walha, Francis Bach +1
Uncertainty quantification is central to the reliable deployment of large language models (LLMs), and eigenvalues of semantic embeddings have recently emerged as a key tool in stat…
Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach
Nassim Walha, Sebastian G. Gruber, Thomas Decker +4
As Large Language Models (LLMs) are increasingly integrated in diverse applications, obtaining reliable measures of their predictive uncertainty has become critically important. A…
A Novel Framework for Uncertainty Quantification via Proper Scores for Classification and Beyond
Sebastian G. Gruber
In this PhD thesis, we propose a novel framework for uncertainty quantification in machine learning, which is based on proper scores. Uncertainty quantification is an important cor…
CARE: Confidence-aware Ratio Estimation for Medical Biomarkers
Jiameng Li, Teodora Popordanoska, Aleksei Tiulpin +3
Ratio-based biomarkers (RBBs), such as the proportion of necrotic tissue within a tumor, are widely used in clinical practice to support diagnosis, prognosis, and treatment plannin…
Optimizing Estimators of Squared Calibration Errors in Classification
Sebastian G. Gruber, Francis Bach
In this work, we propose a mean-squared error-based risk that enables the comparison and optimization of estimators of squared calibration errors in practical settings. Improving t…
Disentangling Mean Embeddings for Better Diagnostics of Image Generators
Sebastian G. Gruber, Pascal Tobias Ziegler, Florian Buettner
The evaluation of image generators remains a challenge due to the limitations of traditional metrics in providing nuanced insights into specific image regions. This is a critical p…
Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors
Teodora Popordanoska, Sebastian G. Gruber, Aleksei Tiulpin +2
Proper scoring rules evaluate the quality of probabilistic predictions, playing an essential role in the pursuit of accurate and well-calibrated models. Every proper score decompos…
A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
Sebastian G. Gruber, Florian Buettner
Generative models, like large language models, are becoming increasingly relevant in our daily lives, yet a theoretical framework to assess their generalization behavior and uncert…
Application-driven Validation of Posteriors in Inverse Problems
Tim J. Adler, Jan-Hinrich Nölke, Annika Reinke +8
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, poste…