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Distributed Statistical Estimation of Matrix Products with Applications

Summary: Develops communication- and round-efficient protocols for estimating ℓp norms, distinct counts, ℓ0-samples, and heavy hitters of a distributed integer matrix product. Unifies these tasks with set-intersection and natural joins, including maximum-intersection search and uniform sampling. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
1743
Venue
PODS
Year
2018
Pagerank
5.093636e-05
Overall Rank
11,894 | 18.40%
DOI
10.1145/3196959.3196964

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BibTeX Citation

@inproceedings{woodruff_pods18,
        address = {New York, NY, USA},
        series = {{PODS} '18},
        title = {{Distributed Statistical Estimation of Matrix Products with Applications}},
        url = {https://dl.acm.org/doi/10.1145/3196959.3196964},
        doi = {10.1145/3196959.3196964},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Woodruff, David P. and Zhang, Qin},
        year = {2018}
}

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