AI RESEARCH

Upper Entropy for 2-Monotone Lower Probabilities

arXiv CS.LG

ArXi:2603.23558v1 Announce Type: new 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. Within credal approaches that consider modeling uncertainty as probability sets, upper entropy plays a central role as an uncertainty measure. This paper is devoted to the computational aspect of upper entropies, providing an exhaustive algorithmic and complexity analysis of the problem.