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English(EN) Sharp Structure-Agnostic Minimax Risk for Partial Linear Models

新研究表征了偏线性模型的最小最大风险

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员表征了偏线性模型中系数估计的尖锐结构无关最小最大风险。这项工作通过定义由近似误差和随机误差预算决定的可用学习器，解决了双重机器学习中的一个开放性问题。研究结果表明，标准的双重机器学习可能高估了目标估计的内在难度，并提出了一个学习器选择原则，该原则在处理非目标学习器时平衡了近似复杂度和随机复杂度。 AI

排序理由 该集群包含一篇发表在 arXiv 上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

新研究表征了偏线性模型的最小最大风险

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该集群包含一篇发表在 arXiv 上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Haichen Hu, David Simchi-Levi ·

    面向部分线性模型的锐利结构无关最小最大风险

    arXiv:2609.07997v1 Announce Type: new Abstract: We characterize the sharp structure-agnostic minimax risk for coefficient estimation in the partial linear model when the outcome and treatment nuisances are learned by two distinct black-box learners, which resolves the open proble…

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