# STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge

@article{Coleman2020STORMFO, title={STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge}, author={Benjamin Coleman and Gaurav Gupta and John Chen and Anshumali Shrivastava}, journal={ArXiv}, year={2020}, volume={abs/2006.14554} }

Empirical risk minimization is perhaps the most influential idea in statistical learning, with applications to nearly all scientific and technical domains in the form of regression and classification models. To analyze massive streaming datasets in distributed computing environments, practitioners increasingly prefer to deploy regression models on edge rather than in the cloud. By keeping data on edge devices, we minimize the energy, communication, and data security risk associated with the…

## 2 Citations

### A One-Pass Distributed and Private Sketch for Kernel Sums with Applications to Machine Learning at Scale

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### Fast Rotation Kernel Density Estimation over Data Streams

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A novel Rotation Kernel is proposed, based on a Rotation Hash method and is much faster to compute, which compresses high dimensional data streams into a small array of integer counters and achieves memory-efficient kernel density estimation over data streams.

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