From the 1 of 11 linked papers with an AI index.
11 papers
Quantification of Credal Uncertainty: A Distance-Based Approach
Xabier Gonzalez-Garcia, Siu Lun Chau, Julian Rodemann +6
The paper introduces a distance-based method using Integral Probability Metrics to quantify total, aleatoric, and epistemic uncertainty for credal sets, providing efficient measure…
Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
Christopher Bülte, Yusuf Sale, Gitta Kutyniok +1
Regression tasks, notably in safety-critical domains, require reliable uncertainty quantification, yet the literature remains largely classification-focused. To address this, we in…
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…
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…
Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity
Thomas Mortier, Alireza Javanmardi, Yusuf Sale +2
Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conform…