安全关键AI系统（如自动驾驶、医疗诊断）的开发者需要量化模型对输入扰动的鲁棒性，XCount提供了一种高效且可扩展的敏感性分析工具，值得关注。
该研究提出了一种量化决策树集成（DTE）模型敏感性的新方法，通过离散化输入空间并枚举易受干扰的区域，计算模型对特征微小变化的敏感程度。方法基于代数决策图（ADD）编码，将问题分解为可组合的子问题，在保证误差和置信度边界的同时实现高效计算。实验表明，工具XCount在多个基准测试中相比传统模型计数方法显著加速，且能随树集成规模扩展。这项工作对安全关键领域的AI验证具有重要意义。
Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach
Decision tree ensembles (DTE) are a popular model for a wide range of AI classification tasks, used in multiple safety critical domains, and hence verifying properties on these models has been an active topic of study over the last decade. One such verification question is the problem of sensitivity, which asks, given a DTE, whether a small change in subset of features can lead to misclassification of the input. In this work, our focus is to build a quantitative notion of sensitivity, tailored to DTEs, by discretizing the input space of the model and enumerating the regions which are susceptible to sensitivity. We propose a novel algorithmic technique that can perform this computation efficiently, within a certified error and confidence bound. Our approach is based on encoding the problem as an algebraic decision diagram (ADD), and further splitting it into subproblems that can be solved efficiently and make the computation compositional and scalable. We evaluate the performance of our technique over benchmarks of varying size in terms of number of trees and depth, comparing it against the performance of model counters over the same problem encoding. Experimental results show that our tool XCount achieves significant speedup over other approaches and can scale well with the increasing sizes of the ensembles.