新方法提高了Cox回归分析的可扩展性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了一种可扩展Cox回归的新方法，解决了大规模数据集中的计算挑战。该方法利用分组风险集和更优的LogSumExp速率分析，改进了先前的优化界限。该方法相对于完整的Cox解决方案实现了T^{-4/5}的均方速率，并与其渐近分布相匹配，在实验中表现良好。 AI

排序理由该集群包含一篇在arXiv上发表的研究论文，详细介绍了一种统计模型的新方法。[lever_c_demoted from research: ic=1 ai=0.4]

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arXiv cs.LG TIER_1 English(EN) · Elizaveta Iashchinskaia, Egor Gladin · 2026-10-02 04:00

通过分组风险集和更快的 LogSumExp 速率实现可扩展的 Cox 回归

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…