提出高效算法计算2-单调下概率的上熵，提升不确定性量化效率。
Upper Entropy for 2-Monotone Lower Probabilities
- 基于2-单调性设计高效算法求解上熵
- 证明问题存在强多项式时间解法
- 适用于模型选择、异常检测等场景
不确定性量化在模型选择/正则化、主动学习或分布外检测等任务中至关重要。在将不确定性建模为概率集的可信度方法中，上熵是核心的不确定性度量。本文聚焦上熵的计算问题，提供了全面的算法与复杂度分析。特别地，我们证明该问题具有强多项式解，并对以往针对2-单调下概率及其特例的算法提出了多项重要改进。
原文摘要 · Abstract (English)
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. In particular, we show that the problem has a strongly polynomial solution, and propose many significant improvements over past algorithms proposed for 2-monotone lower probabilities and their specific cases.
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