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新算法提升学习模型的对抗鲁棒性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新算法，显著提高了学习对对抗样本具有鲁棒性的预测器的能力。该方法在VC维度上实现了线性样本复杂度，比之前的界限有了指数级的改进。该算法将自举聚合（bagging）与鲁棒经验风险最小化（RERM）相结合，在多个自举样本上计算RERM并输出其多数投票。一个互补的下界表明，这种方法对于在此模型中学习是必需的。 AI

影响 这项研究可能导致更具弹性的AI系统，能够抵御对抗性攻击。

排序理由 该集群包含两篇相同的学术论文，详细介绍了一种新的机器学习算法。

在 arXiv stat.ML 阅读 →

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新算法提升学习模型的对抗鲁棒性

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该集群包含两篇相同的学术论文，详细介绍了一种新的机器学习算法。
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报道来源 [2]

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

    Bagging 稳健地学习 VC 类，具有线性样本复杂度

    We revisit the problem of learning predictors robust to adversarial examples at test-time. We prove that VC classes are adversarially robustly learnable with sample complexity linear in the VC dimension $d$, providing an exponential improvement over the previous upper bound of Mo…

  2. arXiv stat.ML TIER_1 English(EN) · Omar Montasser ·

    Bagging 稳健地学习 VC 类，具有线性样本复杂度

    arXiv:2608.13514v1 Announce Type: new Abstract: We revisit the problem of learning predictors robust to adversarial examples at test-time. We prove that VC classes are adversarially robustly learnable with sample complexity linear in the VC dimension $d$, providing an exponential…

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