# Optimally Compressed Nonparametric Online Learning

arXiv: https://arxiv.org/abs/1909.11555 · HTML: https://mlsanity.com/paper/1909.11555/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Localized Adaptive Risk Control](https://mlsanity.com/paper/2405.07976/) (2024)
- [Nonparametric Decentralized Detection and Sparse Sensor Selection via Multi-Sensor Online Kernel Scalar Quantization](https://mlsanity.com/paper/2205.10681/) (2022)
- [An Online Multiple Kernel Parallelizable Learning Scheme](https://mlsanity.com/paper/2308.10101/) (2023)

## Abstract (arXiv)

Batch training of machine learning models based on neural networks is now well established, whereas to date streaming methods are largely based on linear models. To go beyond linear in the online setting, nonparametric methods are of interest due to their universality and ability to stably incorporate new information via convexity or Bayes' Rule. Unfortunately, when used online, nonparametric methods suffer a "curse of dimensionality" which precludes their use: their complexity scales at least with the time index. We survey online compression tools which bring their memory under control and attain approximate convergence. The asymptotic bias depends on a compression parameter that trades off memory and accuracy. Further, the applications to robotics, communications, economics, and power are discussed, as well as extensions to multi-agent systems.