In this work we present an overview of statistical learning, followed by a survey of robust streaming techniques and challenges, culminating in several rigorous results proving the relationship that we motivate and hint at throughout the journey. Furthermore, we unify often disjoint theorems in a shared framework and notation to clarify the deep connections that are discovered. We hope that by approaching these results from a shared perspective, already aware of the technical connections that exist, we can enlighten the study of both fields and perhaps motivate new and previously unconsidered directions of research.

翻译：本文首先概述了统计学习理论，随后综述了鲁棒流式处理技术及其面临的挑战，最终通过一系列严谨的结论证明了我们在全文中所激发并暗示的关联性。此外，我们将多个常被孤立处理的定理统一到共享框架与符号体系下，以阐明所发现的深层联系。我们期望通过这种共享视角的探讨——在已知技术关联的基础上——能够为这两个领域的研究带来启示，并可能激发此前未被考虑的新研究方向。