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English(EN) MMD-Balls as Credal Sets: A PAC-Bayesian Framework for Epistemic Uncertainty in Test-Time Adaptation

新的 PAC-贝叶斯框架量化测试时自适应中的不确定性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个 PAC-贝叶斯框架，用于量化测试时自适应 (TTA) 方法中的认知不确定性。该框架使用源分布和目标分布之间的最大均值差异 (MMD) 来推导泛化界。通过将 MMD-balls 解释为 credal sets，该方法将认知不确定性与偶然不确定性分开，提供了一种有原则的方法来决定何时自适应是有益的。 AI

影响 为理解和量化模型适应新数据分布中的不确定性提供了理论基础。

排序理由 该集群包含一篇详细介绍机器学习新理论框架的学术论文。

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新的 PAC-贝叶斯框架量化测试时自适应中的不确定性

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Ahanaf Hasan Ariq ·

    MMD-Balls作为Credal集：用于测试时适应中认知不确定性的PAC-贝叶斯框架

    arXiv:2605.21783v1 Announce Type: cross Abstract: Test-time adaptation (TTA) methods improve model performance under distribution shift but lack formal guarantees connecting shift magnitude to prediction reliability. We develop a PAC-Bayesian framework yielding generalization bou…

  2. arXiv stat.ML TIER_1 English(EN) · Ahanaf Hasan Ariq ·

    MMD-Balls作为Credal集：用于测试时自适应中认识不确定性的PAC-Bayesian框架

    Test-time adaptation (TTA) methods improve model performance under distribution shift but lack formal guarantees connecting shift magnitude to prediction reliability. We develop a PAC-Bayesian framework yielding generalization bounds explicitly parameterized by the maximum mean d…

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