论文标题
在图中学习的概括范围：调查
Generalization bounds for learning under graph-dependence: A survey
论文作者
论文摘要
传统的统计学习理论依赖于数据相同和独立分布的假设（I.I.D。）。但是，这种假设通常在许多现实生活中不存在。在这项调查中，我们探讨了示例依赖性的学习场景，其依赖关系由依赖图描述，依赖图是一种通常在概率和组合术中使用的模型。我们收集各种图形依赖性浓度边界，然后将其用于得出Rademacher的复杂性和稳定性范围，以从图形依赖性数据中学习。我们通过实践学习任务来说明这种范式，并为将来的工作提供一些研究方向。据我们所知，这项调查是有关此主题的第一个调查。
Traditional statistical learning theory relies on the assumption that data are identically and independently distributed (i.i.d.). However, this assumption often does not hold in many real-life applications. In this survey, we explore learning scenarios where examples are dependent and their dependence relationship is described by a dependency graph, a commonly utilized model in probability and combinatorics. We collect various graph-dependent concentration bounds, which are then used to derive Rademacher complexity and stability generalization bounds for learning from graph-dependent data. We illustrate this paradigm through practical learning tasks and provide some research directions for future work. To our knowledge, this survey is the first of this kind on this subject.
