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New method improves scalability for Cox regression analysis

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Researchers have developed a new method for scalable Cox regression, addressing computational challenges in large-scale datasets. The approach utilizes grouped risk sets and a sharper LogSumExp rate analysis, improving upon previous optimization bounds. This method achieves a mean-square rate of T^{-4/5} relative to the full Cox solution and matches its asymptotic distribution, showing favorable performance in experiments. AI

RANK_REASON The cluster contains a research paper published on arXiv detailing a new method for a statistical model. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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New method improves scalability for Cox regression analysis

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The cluster contains a research paper published on arXiv detailing a new method for a statistical model. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Elizaveta Iashchinskaia, Egor Gladin ·

    Scalable Cox Regression via Grouped Risk Sets and Sharper LogSumExp Rates

    arXiv:2609.40120v1 Announce Type: new Abstract: Motivated by the computational challenges of large-scale Cox regression, we study stochastic minimization of LogSumExp objectives over large sets. Mini-batch normalizer estimates generally yield biased gradients. We instead use a so…

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