credal sets 1integral probability metrics 1multiclass classification 1total variation distance 1uncertainty quantification 1
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cs.AI2026
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…
cs.AI2026
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…