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新方法实现跨域共形预测迁移

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为传输共形校准（TCC）的新颖方法，以解决在仅源域有标记校准数据，但需要在目标域进行预测的共形预测挑战。TCC利用无标记配对观测将标记源校准迁移到目标空间，然后仅使用无标记目标输入来纠正任何残留的不匹配。所提出的方法 TCC-KS 和 weighted-TCC 提供了适应可观察不匹配的有限样本覆盖保证，证明了在 CIFAR-100-C 和 Tiny-ImageNet-C 等数据集上无需标记目标校准数据即可可靠迁移。 AI

影响 在发生域偏移时，能够为机器学习模型提供更鲁棒的不确定性量化。

排序理由 详细介绍一种新机器学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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新方法实现跨域共形预测迁移

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详细介绍一种新机器学习方法的学术论文。[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) · Achref Doula ·

    Conformal Calibration Transfer

    arXiv:2609.10737v1 Announce Type: cross Abstract: Conformal prediction converts point predictions into set-valued predictions with coverage guarantees under exchangeability between calibration and deployment data. We study conformal calibration transfer, where this requirement fa…

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