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用于平滑损失函数的新型PAC-Bayes去随机化方法

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了一种新的方法，用于去随机化PAC-Bayes泛化界，特别适用于平滑损失函数。该方法旨在通过利用损失函数和预测器类别的平滑特性，为确定性预测器创建高概率界。研究详细介绍了从Gibbs预测器过渡到后验均值确定性预测器的成本，将其与Jensen差距类的泛化差距联系起来，并提出了一个受理论框架启发的实用正则化器。 AI

影响 这项研究可能为机器学习模型带来更鲁棒的泛化界，从而可能提高它们在实际应用中的可靠性。

排序理由 该集群包含一篇详细介绍机器学习新理论方法的学术论文。

在 arXiv stat.ML 阅读 →

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用于平滑损失函数的新型PAC-Bayes去随机化方法

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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Alexandre Lemire Paquin, Brahim Chaib-Draa, Philippe Gigu\`ere ·

    基于平滑性的PAC-Bayes界限的去随机化

    arXiv:2606.19105v1 Announce Type: new Abstract: We study PAC-Bayes derandomization for smooth loss functions. Our goal is to obtain generalization bounds that hold with high probability for deterministic predictors by exploiting smoothness properties of both the loss and the pred…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    基于平滑性的PAC-Bayes界限的去随机化

    We study PAC-Bayes derandomization for smooth loss functions. Our goal is to obtain generalization bounds that hold with high probability for deterministic predictors by exploiting smoothness properties of both the loss and the predictor class. We show that passing from the Gibbs…

  3. arXiv stat.ML TIER_1 English(EN) · Philippe Giguère ·

    基于平滑性的PAC-Bayes界面的去随机化

    We study PAC-Bayes derandomization for smooth loss functions. Our goal is to obtain generalization bounds that hold with high probability for deterministic predictors by exploiting smoothness properties of both the loss and the predictor class. We show that passing from the Gibbs…

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