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New distributed sketching method for OLS regression reduces computational cost

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for distributed sketching in ordinary least squares (OLS) regression. This approach involves creating small sketches of large datasets across multiple machines, allowing for separate construction and averaging of OLS estimators. The study focuses on sketching on partitioned subsets to further reduce computational costs, characterizing the exact excess loss of the averaged OLS estimator. Results indicate that this loss is comparable to traditional methods when subset covariance divergence is minimal. AI

IMPACT This research could lead to more efficient distributed training of machine learning models that rely on OLS regression.

RANK_REASON The cluster contains an academic paper detailing a new method for OLS regression. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New distributed sketching method for OLS regression reduces computational cost

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The cluster contains an academic paper detailing a new method for OLS regression. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Luyuan Yang, Brayden Garner, Shayan Shafaei, Chao Lan ·

    Distributed Sketching on Data Partitions for OLS Regression

    arXiv:2607.07888v1 Announce Type: new Abstract: This paper studies distributed sketching for ordinary least squares (OLS) regression, an approach that distributes small sketches of a large data set over multiple machines to separately construct OLS estimators and average them. Un…

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