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The Cartographer
On Low-Risk Heavy Hitters and Sparse Recovery Schemes
September 09, 2017 Β· Declared Dead Β· π International Workshop and International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
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Authors
Yi Li, Vasileios Nakos, David Woodruff
arXiv ID
1709.02919
Category
cs.DS: Data Structures & Algorithms
Citations
15
Venue
International Workshop and International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
Last Checked
3 months ago
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.
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