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English(EN) Occam's Razor is Only as Sharp as Your ELBO

通过ELBO进行的贝叶斯模型选择可能导致过拟合，提醒从业者注意

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新论文探讨了证据下界（ELBO）与贝叶斯模型选择中的奥卡姆剃刀之间的关系。研究表明，基于ELBO的超参数学习可能导致过拟合，这与偏好更简单模型的奥卡姆剃刀原则相悖。令人惊讶的是，使用证据本身的贝叶斯模型选择有时会偏好过拟合模型，而ELBO则不会。研究结果表明，从业者应警惕降秩假设（在大模型中为便于处理而必需）如何影响模型选择。 AI

影响 强调了大型贝叶斯模型在模型选择中潜在的陷阱，影响了该领域的从业者。

排序理由 关于贝叶斯推断和模型选择的理论方面的学术论文。

在 arXiv stat.ML 阅读 →

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通过ELBO进行的贝叶斯模型选择可能导致过拟合，提醒从业者注意

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关于贝叶斯推断和模型选择的理论方面的学术论文。
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Ethan Harvey, Michael C. Hughes ·

    奥卡姆剃刀的锋利度取决于你的ELBO

    arXiv:2604.25984v1 Announce Type: new Abstract: The marginal likelihood, also known as the evidence, is regarded as a mathematical embodiment of Occam's razor, enabling model selection that avoids overfitting. The evidence lower bound (ELBO) objective from variational inference h…

  2. arXiv stat.ML TIER_1 English(EN) · Michael C. Hughes ·

    奥卡姆剃刀的锋利程度取决于你的ELBO

    The marginal likelihood, also known as the evidence, is regarded as a mathematical embodiment of Occam's razor, enabling model selection that avoids overfitting. The evidence lower bound (ELBO) objective from variational inference has also been used for similar purposes. Prior wo…

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