2 citations · 2 across the 7 of their papers we have counts for
13 papers
What Uncertainties Do We Need for Dynamical Systems?
Yusuf Sale, Christopher Bülte, Felix Czaja +2
The distinction between aleatoric and epistemic uncertainty has received considerable attention in machine learning research, mainly in the context of supervised learning but also…
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to co…
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.…
Quantification of Credal Uncertainty: A Distance-Based Approach
Xabier Gonzalez-Garcia, Siu Lun Chau, Julian Rodemann +6
Credal sets, i.e., closed convex sets of probability measures, provide a natural framework to represent aleatoric and epistemic uncertainty in machine learning. Yet how to quantify…
Quantifying Epistemic Predictive Uncertainty in Conformal Prediction
Siu Lun Chau, Soroush H. Zargarbashi, Yusuf Sale +1
We study the problem of quantifying epistemic predictive uncertainty (EPU) -- that is, uncertainty faced at prediction time due to the existence of multiple plausible predictive mo…
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…