# Robust Streaming, Sampling, and a Perspective on Online Learning

arXiv: https://arxiv.org/abs/2312.01634 · HTML: https://mlsanity.com/paper/2312.01634/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Adversarial Laws of Large Numbers and Optimal Regret in Online Classification](https://mlsanity.com/paper/2101.09054/) (2021)
- [A Framework for Adversarially Robust Streaming Algorithms](https://mlsanity.com/paper/2003.14265/) (2020)
- [The Adversarial Robustness of Sampling](https://mlsanity.com/paper/1906.11327/) (2019)
- [Tight Bounds for Adversarially Robust Streams and Sliding Windows via Difference Estimators](https://mlsanity.com/paper/2011.07471/) (2020)
- [Optimal streaming and tracking distinct elements with high probability](https://mlsanity.com/paper/1804.01642/) (2018)

## Abstract (arXiv)

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.