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English(EN) Multi-source conformal prediction: leveraging heterogeneity via localization

新研究解决一致性预测方法中的数据偏移问题

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文探讨了处理数据偏移的高级一致性预测技术。第一篇论文介绍了多源随机局部化一致性预测（MS-RLCP），以利用来自异构数据源的互补信息，即使在测试分布与单个源显著不同的情况下也能建立覆盖界限。第二篇论文提出了一种用于分割一致性预测的标签偏移调整贝叶斯分数（LSA score），该方法通过重新加权源预测器来调整贝叶斯一致性方法，以在标签偏移下保持覆盖率。该LSA分数旨在与现有方法相比，产生更短、更准确的预测区间。 AI

影响 这些方法为应对现实世界数据偏移的AI模型提供了改进的不确定性量化。

排序理由 该集群包含两篇详细介绍一致性预测新方法的学术论文。

在 arXiv cs.LG 阅读 →

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新研究解决一致性预测方法中的数据偏移问题

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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Sylvain Rousseau (Heudiasyc), Soundouss Messoudi (Heudiasyc) ·

    多目标回归的尺度分数一致性预测

    arXiv:2609.17091v1 Announce Type: new Abstract: Multi-target regression requires a model to simultaneously predict several related outputs. Conformal prediction provides distribution-free, finite-sample marginal coverage guarantees, but extending these to joint multi-dimensional …

  2. arXiv cs.LG TIER_1 English(EN) · Rohan Hore, Anirban Chatterjee, Sayantan Choudhury ·

    多源一致性预测：通过局部化利用异质性

    arXiv:2609.14531v1 Announce Type: cross Abstract: Many modern prediction tasks involve data from multiple heterogeneous sources, while the test distribution may differ substantially from any individual source. Although heterogeneity poses challenges, it also offers an opportunity…

  3. arXiv cs.LG TIER_1 English(EN) · Hyeonsu Lee, Juyeon Kim, Erkhembayar Jadamba, Seungjin Choi, Hyunjin Shin ·

    带有标签偏移调整的贝叶斯得分的分裂保形预测

    arXiv:2609.12386v1 Announce Type: new Abstract: Conformal prediction provides distribution-free uncertainty quantification under exchangeability. However, this assumption is violated by label shift, where the marginal distribution of labels changes while the conditional distribut…

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