English(EN) Structure-Aware Graph Abstention for Reliable Selective Forecasting

新方法通过结构感知图弃权法改进选择性预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了一种名为结构感知图弃权法（SAGA）的新方法，以改进选择性预测。该技术在保持覆盖率预算的同时，避免对高风险测试窗口进行预测。与之前对整个预测进行评分的方法不同，SAGA区分了实例级可行性与多变量输出之间的关系一致性。它使用学习到的稀疏图和结构能量度量来操作化关系一致性，并通过误差加权图正则化和分数-误差对齐进行训练。 AI

影响这种新方法通过确保预测的内部一致性，可以提高预测系统的可靠性。

排序理由该条目是一篇学术论文，详细介绍了一种新的选择性预测方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Jianxiang Xie, Belal Alsinglawi · 2026-10-07 04:00

面向可靠选择性预测的结构感知图抽象

arXiv:2610.08322v1 Announce Type: new Abstract: Selective forecasting abstains on high-risk test windows under a retained-coverage budget. Existing gates such as TEM (Brusokas et al., 2025) score each forecast as a whole; for multivariate outputs, trajectories can look plausible …

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面向可靠选择性预测的结构感知图抽象

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