English(EN) Generalized Hierarchical Conformal Prediction

新方法GHCP增强了基于组的预测集

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-18 04:00

研究人员开发了广义分层一致性预测（GHCP），这是一种在数据以组为单位收集时创建预测集的新方法。标准的层级一致性预测在目标组的少数观测值已可用时会遇到困难，因为其对称性条件被违反。GHCP通过为测试组分配一个随机捐赠的参考组大小来解决这个问题，恢复了一致性推理所需的对称性。此外，GHCP利用初始测试组的观测值来提高不一致分数的质量，并引入了一个变体，通过限制合格捐赠者来提高效率。 AI

影响增强了分组数据的预测推理能力，有可能提高具有分层结构的应用中的模型准确性。

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arXiv stat.ML TIER_1 English(EN) · Soham Mallick, Eric Tchetgen Tchetgen, Edgar Dobriban, Yonghoon Lee · 2026-08-18 04:00

广义分层一致性预测

arXiv:2608.15500v1 Announce Type: cross Abstract: Many prediction problems arise with data collected in groups. In this setting, hierarchical conformal prediction (HCP) (Lee et al., 2026) provides distribution-free prediction sets for a new observation from a previously unseen gr…

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广义分层一致性预测

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