On Low-Risk Heavy Hitters and Sparse Recovery Schemes
Data Structures and Algorithms
2019-12-10 v3
Abstract
We study the heavy hitters and related sparse recovery problems in the low-failure probability regime. This regime is not well-understood, and has only been studied for non-adaptive schemes. The main previous work is one on sparse recovery by Gilbert et al.(ICALP'13). We recognize an error in their analysis, improve their results, and contribute new non-adaptive and adaptive sparse recovery algorithms, as well as provide upper and lower bounds for the heavy hitters problem with low failure probability.
Keywords
Cite
@article{arxiv.1709.02919,
title = {On Low-Risk Heavy Hitters and Sparse Recovery Schemes},
author = {Yi Li and Vasileios Nakos and David Woodruff},
journal= {arXiv preprint arXiv:1709.02919},
year = {2019}
}