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English(EN) Conditional Predictive Inference for General Structured Data with Group Symmetries

新的C-SymmPI框架为结构化数据提供条件预测推断

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种名为C-SymmPI的新框架，用于条件预测推断，该框架专为具有群对称性的结构化数据而设计。该方法超越了典型的可交换性假设，为网络和集群等复杂数据类型提供了近乎条件的覆盖保证。C-SymmPI将条件覆盖重新表述为错误覆盖误差，并提供了理论保证，其实证结果显示与现有方法相比，准确性和稳定性均有所提高。 AI

影响 增强了结构化数据的量化不确定性，有可能提高AI模型在复杂领域的可靠性。

排序理由 该集群包含一篇详细介绍新统计方法的学术论文。

在 arXiv stat.ML 阅读 →

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新的C-SymmPI框架为结构化数据提供条件预测推断

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Yichen Shen, Mengxin Yu ·

    具有群对称性的通用结构化数据的条件预测推断

    arXiv:2605.17934v1 Announce Type: cross Abstract: We study distribution-free predictive inference for data with group symmetries, aiming to establish near-conditional coverage guarantees beyond exchangeability for structured data. While many predictive inference methods achieve a…

  2. arXiv stat.ML TIER_1 English(EN) · Mengxin Yu ·

    具有群对称性的通用结构化数据的条件预测推理

    We study distribution-free predictive inference for data with group symmetries, aiming to establish near-conditional coverage guarantees beyond exchangeability for structured data. While many predictive inference methods achieve a target coverage level, most provide marginal cove…

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