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Get the Most out of Your Sample: Optimal Unbiased Estimators using Partial Information

Summary: Method to derive optimal unbiased estimators for multi-instance queries (quantiles, ranges, subset-sums) that exploit partial information across sampled instances, improving on per-instance Horvitz–Thompson. Prove variance optimality and show large empirical gains. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1532
Venue
PODS
Year
2011
Pagerank
5.093636e-05
Overall Rank
12,362 | 15.19%
DOI
10.1145/1989284.1989288

Incoming Non-self Citations Over Time

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Authors

BibTeX Citation

@inproceedings{cohen_pods11,
        address = {New York, NY, USA},
        series = {{PODS} '11},
        title = {{Get the Most out of Your Sample: Optimal Unbiased Estimators using Partial Information}},
        url = {https://dl.acm.org/doi/10.1145/1989284.1989288},
        doi = {10.1145/1989284.1989288},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Cohen, Edith and Kaplan, Haim},
        year = {2011}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
8,379 Sampling Big Ideas in Query Optimization 2023 PODS 5.4378437e-05
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