credal sets 1integral probability metrics 1multiclass classification 1total variation distance 1uncertainty quantification 1
From the 1 of 3 linked papers with an AI index.
3 papers
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.GT2026
An Enriched Model of Strategic Voting under Uncertainty
Henri Surugue, Sébastien Destercke
We present a new strategic voting model where we use uncertainty representation to model preferences. Specifically, we use probability sets as uncertainty representations, together…
cs.LG2026
Upper Entropy for 2-Monotone Lower Probabilities
Tuan-Anh Vu, Sébastien Destercke, Frédéric Pichon
Uncertainty quantification is a key aspect in many tasks such as model selection/regularization, or quantifying prediction uncertainties to perform active learning or OOD detection…