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English(EN) SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination

SHIFT估计器改进了重尾数据的鲁棒双重机器学习

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了SHIFT，一种用于双重机器学习（DML）管道的新型鲁棒估计器，旨在处理重尾数据污染。SHIFT结合了交叉拟合干扰正交化、核局部Welsch损失第二阶段和防御性普通最小二乘法重新拟合。该方法在存在异常值的情况下显著提高了准确性，在压力测试中将均方根误差（RMSE）从1.03降低到0.33，并实现了异常值掩码恢复的高F1分数。 AI

影响 引入了一种鲁棒的统计方法来处理机器学习管道中被污染的数据，有可能提高在实际应用中的可靠性。

排序理由 这是一篇详细介绍一种新的统计估计方法的学术论文。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

SHIFT估计器改进了重尾数据的鲁棒双重机器学习

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Eichi Uehara ·

    SHIFT：重尾污染下平均剂量-响应函数的鲁棒双重机器学习

    arXiv:2605.00176v1 Announce Type: cross Abstract: Double-machine-learning pipelines for the Average Dose-Response Function rely on kernel-weighted local-linear smoothers, which inherit unbounded functional influence: a single outlier within a kernel window biases the curve across…

  2. arXiv stat.ML TIER_1 English(EN) · Eichi Uehara ·

    SHIFT：重尾污染下平均剂量-响应函数的鲁棒双重机器学习

    Double-machine-learning pipelines for the Average Dose-Response Function rely on kernel-weighted local-linear smoothers, which inherit unbounded functional influence: a single outlier within a kernel window biases the curve across the entire window. We introduce SHIFT (Self-calib…

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