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English(EN) ProxyGuard: Direct Reliability Inference for Randomized Data Release Mechanisms with Shared Targets

ProxyGuard 方法增强了机器学习数据发布的可靠性

作者 PulseAugur 编辑部 · [1 个来源] ·

一种名为 ProxyGuard 的新方法已被开发出来，用于提高机器学习中数据发布机制的可靠性。该系统允许研究人员通过控制可能使不充分的发布显得足够的错误，更有信心地选择代理数据集。ProxyGuard 在不需要独立的目標批次或对数据发布依赖性的假设的情况下，提供了机制可靠性保证，增强了前瞻性审计的能力。 AI

影响 提高了在机器学习研究和开发中使用代理数据集的信心。

排序理由 详细介绍机器学习数据可靠性新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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ProxyGuard 方法增强了机器学习数据发布的可靠性

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详细介绍机器学习数据可靠性新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Dipesh Tharu Mahato, Pramod Dhungana ·

    ProxyGuard：用于具有共享目标的随机数据发布机制的直接可靠性推理

    arXiv:2608.18643v1 Announce Type: cross Abstract: Researchers often choose a proxy dataset from many releases, transformations, or seeds. Search can make an invalid release appear adequate, while one adequate release does not establish that its generator is reliable. ProxyGuard c…

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