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Efficient Approximate Algorithms for Empirical Entropy and Mutual Information

Summary: Approximate top-k and filtering for empirical entropy and mutual information with tunable accuracy-time trade-offs. Introduces stopping rules and theoretical bounds, yielding large runtime reductions on real datasets while preserving accurate results and outperforming prior approaches. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6208
Venue
SIGMOD
Year
2021
Pagerank
5.5404259e-05
Overall Rank
7,807 | 46.44%
DOI
10.1145/3448016.3457255

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{chen_sigmod21,
        title = {{Efficient Approximate Algorithms for Empirical Entropy and Mutual Information}},
        author = {Chen, Xingguang and Wang, Sibo},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457255},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457255},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
885 Finding Frequent Items in Data Streams 2008 VLDB 0.00013419017
2,225 HubPPR: Effective Indexing for Approximate Personalized PageRank 2017 VLDB 8.9183159e-05
5,741 Efficient Algorithms for Finding Approximate Heavy Hitters in Personalized PageRanks 2018 SIGMOD 6.1029326e-05
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