English(EN) Multiply Robust Conformal Risk Control with Coarsened Data

新框架处理缺失数据下的预测推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一个新的预测推理框架，该框架可以处理训练集和校准集中的粗糙或缺失数据。所提出的方法，即鲁棒分裂预测（RSP）和鲁棒分裂共识预测（RSCP），将半参数理论与共识方法相结合，以提供正式的覆盖保证。这些技术适用于各种缺失模式，包括多阶段单调和非单调场景，并通过模拟和类风湿关节炎研究中的实际应用得到了证明。 AI

影响这项研究可以提高在数据不完整或不完美等现实场景中AI模型的可靠性。

排序理由这是一篇详细介绍处理预测建模中数据问题的新的统计框架的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Manit Paul, Arun Kumar Kuchibhotla, Eric J. Tchetgen Tchetgen · 2026-10-23 04:00

Multiply Robust Conformal Risk Control with Coarsened Data

arXiv:2508.15489v2 Announce Type: replace-cross Abstract: Conformal Prediction (CP) provides prediction sets with formal coverage guarantees, but most existing methods assume fully observed training and calibration data. In many applications, however, outcomes, covariates, or bot…

本文如何被排名

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Multiply Robust Conformal Risk Control with Coarsened Data

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