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New algorithms improve private statistical estimation with tunable trade-offs

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Researchers have developed new differentially private algorithms for estimating monotone statistics, which are statistics that remain consistent when new data is added. The proposed algorithms improve upon the traditional subsample-and-aggregate method by reducing sample complexity by a factor of 't' while increasing running time by a factor of 'e^t', where 't' is a tunable parameter. These advancements have applications in areas such as private eigenvalue estimation and estimating parameters in high-dimensional models like linear regression. The work also includes a query-complexity lower bound demonstrating the near-optimality of the new algorithms. AI

RANK_REASON This is a research paper detailing new algorithms for private statistical estimation. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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New algorithms improve private statistical estimation with tunable trade-offs

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This is a research paper detailing new algorithms for private statistical estimation. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Gavin Brown, Ephraim Linder, Mahbod Majid, Vikrant Singhal ·

    Privately Estimating Monotone Statistics in Polynomial Time

    arXiv:2605.27912v1 Announce Type: cross Abstract: We study efficient differentially private algorithms for estimating monotone statistics, i.e., statistics that are monotone under the addition of new observations. The starting point for our investigation is subsample-and-aggregat…

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