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English(EN) On the QUEST for Uncertainty Quantification via Highest Density Regions

新的QUEST框架在机器学习中提供了改进的不确定性量化

作者 PulseAugur 编辑部 · [1 个来源] ·

一种名为QUEST（Quantifying Uncertainty via highest dEnSiTy regions）的新框架已被提出用于机器学习中的不确定性量化。该方法通过分布支撑集中最可能子集的体积来表征不确定性，为基于适当评分规则的方法提供了一种替代方案。QUEST度量已被证明满足不确定性量化的关键公理，并在选择性预测基准测试中与方差和微分熵等标准度量相比表现良好。 AI

影响 通过改进不确定性估计，为安全关键型AI应用中更可靠的决策提供了一个新颖的框架。

排序理由 该集群包含一篇详细介绍机器学习中不确定性量化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的QUEST框架在机器学习中提供了改进的不确定性量化

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该集群包含一篇详细介绍机器学习中不确定性量化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sam Goring, Tom Kuipers, Nicola Paoletti, David S. Watson ·

    在最高密度区域进行不确定性量化探索

    arXiv:2606.19569v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is essential for reliable decision-making in safety-critical applications in probabilistic machine learning. For regression problems, dominant scalar UQ approaches - notably, those based on proper sco…

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