English(EN) Neural Variational Cut Posteriors without Upstream Data

新的NeVI-Cut方法可在无上游数据的情况下实现不确定性传播

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员开发了NeVI-Cut，一种用于割贝叶斯问题的新型神经变分推理方法。该方法允许在下游分析中传播参数不确定性，而无需访问原始上游数据或模型。NeVI-Cut利用条件归一化流和期望 Kullback-Leibler 散度的样本平均近似来实现计算效率和准确性。该方法已在各种应用中证明了其速度和有效性，并具有收敛率的理论保证。 AI

影响能够更有效地在复杂的机器学习管道中进行不确定性传播。

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在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Jiafang Song, Sandipan Pramanik, Abhirup Datta · 2026-09-03 04:00

无上游数据的神经变分切割后验

arXiv:2510.10268v3 Announce Type: replace Abstract: In many applications, one must propagate parameter uncertainty from an earlier (upstream) analysis, available as samples, to subsequent (downstream) analyses without feedback. This problem is called cutting feedback or cut-Bayes…

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无上游数据的神经变分切割后验

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