English(EN) Online simultaneous inference for quantiles via smoothed stochastic gradient descent

新的SGD方法支持具有理论保证的在线分位数估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

本文介绍了一种新颖的平滑随机梯度下降（SGD）算法，用于在线分位数估计。该方法确保估计值在整个流数据过程中相对于分位数水平保持单调。该研究提供了尾部概率界限的理论保证，并提供了一种在线乘数自举方法，用于跨坐标和分位数水平进行有效的同步推断。 AI

影响引入了一种新颖的在线分位数估计统计方法，可能改进机器学习应用中的实时数据分析。

排序理由详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 80 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
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arXiv stat.ML TIER_1 English(EN) · Likai Chen, Georg Keilbar, Wei Biao Wu · 2026-09-02 04:00

基于平滑随机梯度下降的在线分位数同步推理

arXiv:2505.13299v2 Announce Type: replace Abstract: This paper considers the estimation of quantiles via a smoothed version of the stochastic gradient descent (SGD) algorithm. By smoothing the score function with a bandwidth tied to the learning rate, we obtain estimates that are…