研究人员开发了一个新的共形预测框架，用于二元回归，特别解决了复杂的缺失数据场景。理论进展包括在较弱的不变性条件下建立超均匀性，并处理索引集的随机子集样本。所提出的方法即使在缺失非随机（missing-not-at-random）假设下，也为加权共形预测提供了渐近有效性，这是一个重要的理论贡献。 AI
排序理由 该集群包含一篇发表在arXiv上的研究论文，详细介绍了共形预测的理论进展。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
新的共形预测框架应对复杂缺失数据
报道来源 [2]
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Conformal Prediction for Dyadic Regression Under Complex Missingness
arXiv:2606.11136v1 Announce Type: cross Abstract: We develop a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms. At the theoretical level, we establish super-uniformity of conformal prediction under distributional invariance co…
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Conformal Prediction for Dyadic Regression Under Complex Missingness
We develop a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms. At the theoretical level, we establish super-uniformity of conformal prediction under distributional invariance conditions weaker than exchangeability. A key result…