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Weighted Minwise Hashing Beats Linear Sketching for Inner Product Estimation

Summary: Weighted MinHash-based compact sketches for independent pairwise inner-product estimation, provably matching linear sketches on dense vectors and improving error bounds for sparse vectors with limited support overlap. Empirically outperforms CountSketch/JL and unweighted hashing, making it attractive for dataset-search and column-wise covariance/conditional-mean estimation. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1898
Venue
PODS
Year
2023
Pagerank
4.1945683e-05
Overall Rank
11,168 | 22.31%
DOI
10.1145/3584372.3588679

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Rank Citing Paper Year Venue Pagerank
11,025 Sampling Methods for Inner Product Sketching 2024 VLDB 4.1945683e-05
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