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新的主动学习算法应对对抗性图损坏

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的主动学习算法，旨在识别图中的损坏顶点，即使在对手篡改网络结构的情况下也能做到。该算法旨在通过最少数量的标签查询来有效地找到这些隐藏的顶点。其查询复杂度多项式地依赖于对手的能力和图的顶点扩展度（一种连通性度量）。这项工作强调了顶点扩展度在能够抵抗结构性对抗性攻击的主动学习算法中的关键作用。 AI

影响 这项研究可能导致更强大的基于图的AI系统，能够检测和减轻对抗性操纵。

排序理由 该集群包含一篇在arXiv上发表的详细介绍一种新算法的研究论文。

在 arXiv stat.ML 阅读 →

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新的主动学习算法应对对抗性图损坏

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Marco Bressan, Nicol\`o Cesa-Bianchi, Tommaso d`Orsi, Emmanuel Esposito, Silvio Lattanzi ·

    Active Learning on Adversarially Corrupted Graphs

    arXiv:2607.04869v1 Announce Type: cross Abstract: Motivated by real-world scenarios where malicious entities tamper with existing networks, we define a model where an adversary seeks to hide a set of \emph{corrupted vertices} inside a graph $G^*$. To this end, the adversary can a…

  2. arXiv stat.ML TIER_1 English(EN) · Silvio Lattanzi ·

    Active Learning on Adversarially Corrupted Graphs

    Motivated by real-world scenarios where malicious entities tamper with existing networks, we define a model where an adversary seeks to hide a set of \emph{corrupted vertices} inside a graph $G^*$. To this end, the adversary can add edges between the corrupted vertices, as well a…

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