11 citations · 14 across the 9 of their papers we have counts for
10 papers
Efficient Credal Prediction through Decalibration
Paul Hofman, Timo Löhr, Maximilian Muschalik +2
A reliable representation of uncertainty is essential for the application of modern machine learning methods in safety-critical settings. In this regard, the use of credal sets (i.…
Uncertainty Quantification for Machine Learning: One Size Does Not Fit All
Paul Hofman, Yusuf Sale, Eyke Hüllermeier
Proper quantification of predictive uncertainty is essential for the use of machine learning in safety-critical applications. Various uncertainty measures have been proposed for th…
Uncertainty Quantification with Proper Scoring Rules: Adjusting Measures to Prediction Tasks
Paul Hofman, Yusuf Sale, Eyke Hüllermeier
We address the problem of uncertainty quantification and propose measures of total, aleatoric, and epistemic uncertainty based on a known decomposition of (strictly) proper scoring…
Credal Prediction based on Relative Likelihood
Timo Löhr, Paul Hofman, Felix Mohr +1
Predictions in the form of sets of probability distributions, so-called credal sets, provide a suitable means to represent a learner's epistemic uncertainty. In this paper, we prop…
An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
Christopher Bülte, Yusuf Sale, Timo Löhr +3
Uncertainty quantification is crucial in machine learning, yet most (axiomatic) studies of uncertainty measures focus on classification, leaving a gap in regression settings with l…
Label-wise Aleatoric and Epistemic Uncertainty Quantification
Yusuf Sale, Paul Hofman, Timo Löhr +3
We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. This label-wise perspective allows unce…
Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules
Paul Hofman, Yusuf Sale, Eyke Hüllermeier
Uncertainty representation and quantification are paramount in machine learning and constitute an important prerequisite for safety-critical applications. In this paper, we propose…
Second-Order Uncertainty Quantification: Variance-Based Measures
Yusuf Sale, Paul Hofman, Lisa Wimmer +2
Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process…
Conformal Prediction with Partially Labeled Data
Alireza Javanmardi, Yusuf Sale, Paul Hofman +1
While the predictions produced by conformal prediction are set-valued, the data used for training and calibration is supposed to be precise. In the setting of superset learning or…
Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?
Lisa Wimmer, Yusuf Sale, Paul Hofman +2
The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and mutual information, respectively, has recently become quite common in machine learning…