论文标题
极端因果推断的热带几何形状
The tropical geometry of causal inference for extremes
论文作者
论文摘要
极值统计是经典统计的最大类似物，而热带几何形状是经典几何形状的最大类似物。在本文中，我们回顾了最近的工作，其中使用热带几何形状的见解来开发新的，有效的学习算法，并在极值统计中在基准数据集上具有领先的性能。我们提供了直觉，并在基准数据集上的性能下支持，原因是为什么以及何时在经典方法上采用了极端的因果推断。最后，我们在因果推理，热带几何学和深度学习的交集中列出了一些开放问题。
Extreme value statistics is the max analogue of classical statistics, while tropical geometry is the max analogue of classical geometry. In this paper, we review recent work where insights from tropical geometry were used to develop new, efficient learning algorithms with leading performance on benchmark datasets in extreme value statistics. We give intuition, backed by performances on benchmark datasets, for why and when causal inference for extremes should be employed over classical methods. Finally, we list some open problems at the intersection of causal inference, tropical geometry and deep learning.
