English(EN) Selective Prediction Intervals: Jointly Learning to Predict, Bound, and Abstain

新的SelPI方法实现了联合预测、区间边界和弃权

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

一篇新的研究论文介绍了一种名为SelPI的方法，该方法联合训练深度学习模型以提供点估计、预测区间和弃权选项。与现有孤立提供这些功能的方法不同，SelPI使用单一损失函数，并带有明确的接受率预算。在18个表格基准测试上的实验表明，SelPI学习到的弃权机制在选择可靠预测方面优于依赖区间宽度或深度集成不一致性的方法。 AI

影响通过实现联合预测、区间边界和弃权来增强模型可靠性，有可能改善高风险应用中的决策。

排序理由该集群包含一篇详细介绍新机器学习方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Signal score: 26 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Lidor Rubi, Gilad Katz · 2026-11-05 05:00

选择性预测区间：联合学习预测、约束和弃权

arXiv:2611.06158v1 Announce Type: new Abstract: Many high-stakes regression decisions ask three things of a model at once: a point estimate to act on, an interval that bounds the risk of acting, and the option to abstain when neither can be trusted. Deep learning provides these i…

本文如何被排名

报道来源 [1]

选择性预测区间：联合学习预测、约束和弃权

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