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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
6147
Venue
SIGMOD
Year
2021
Pagerank
4.6135329e-05
Overall Rank
7,915 | 45.00%
DOI
10.1145/3448016.3457255

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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
831 Finding Frequent Items in Data Streams 2008 VLDB 0.00016094846
2,110 HubPPR: Effective Indexing for Approximate Personalized PageRank 2017 VLDB 9.5280826e-05
6,310 Efficient Algorithms for Finding Approximate Heavy Hitters in Personalized PageRanks 2018 SIGMOD 5.111823e-05
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