Generalization Bounds for Learning from Graph-Dependent Data

This survey explores generalization bounds for learning from graph-dependent data, where the dependencies among examples are described by a dependency graph. It presents concentration inequalities and uses them to derive Rademacher complexity and algorithmic stability generalization bounds for learning from such interdependent data.