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New research shows fresh sketching accelerates ridge regression

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv explores the advantages of using 'fresh sketching' in ridge regression, a technique used to accelerate large-scale regression problems. The paper demonstrates that drawing fresh randomness at each step, rather than reusing the same sketch, leads to sharper convergence guarantees and faster convergence rates. Experiments conducted on synthetic and real-world data, including tests on Qwen2.5 representations, support the theoretical findings. AI

IMPACT This research could lead to more efficient training of large-scale models by improving regression techniques.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new theoretical advancement and experimental validation in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New research shows fresh sketching accelerates ridge regression

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The cluster contains a research paper published on arXiv detailing a new theoretical advancement and experimental validation in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Linkai Ma, Qilin Li, Petros Drineas ·

    The Advantages of Fresh Sketching for Ridge Regression

    arXiv:2609.38565v1 Announce Type: new Abstract: Over the past 25 years, sketching and sampling have become widely used tools for accelerating large-scale regression. In iterative randomized solvers, a basic design choice is whether to $\textit{reuse}$ the same sketch or draw $\te…

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