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New Algorithms for Monotone Classification

Summary: Active model: exact optimal needs Ω(n) label probes even for d=1; randomized (1+ε)-approximation uses Õ(w/ε^2) probes (w = dominance width), matching lower bounds up to polylog factors. Passive model: optimal monotone classifier found in poly(n,d) time. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1851
Venue
PODS
Year
2021
Pagerank
5.093636e-05
Overall Rank
11,636 | 20.17%
DOI
10.1145/3452021.3458324

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BibTeX Citation

@inproceedings{tao_pods21,
        address = {New York, NY, USA},
        series = {{PODS} '21},
        title = {{New Algorithms for Monotone Classification}},
        url = {https://dl.acm.org/doi/10.1145/3452021.3458324},
        doi = {10.1145/3452021.3458324},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Tao, Yufei and Wang, Yu},
        year = {2021}
}

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