本文研究了因果不变性在领域自适应场景下改进机器学习模型的效用，特别是在目标样本有限且带标签数据不足的情况下。研究聚焦于线性回归，推导了理论界限，表明有限样本增益取决于候选预测变量之间的间隔和估计误差。研究结果表明，如果这些间隔足够大，因果知识可以加速学习；但如果间隔太小，则没有优势。 AI
排序理由 该聚类包含一篇讨论机器学习理论方面的学术论文。
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因果不变性在机器学习领域自适应中的应用探讨
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How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
arXiv:2606.12680v1 Announce Type: cross Abstract: Machine learning models often degrade when they are deployed on a target distribution that differs from the source distributions they were trained on. Recent work in causality-based domain generalization has shown how shared causa…
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How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
Machine learning models often degrade when they are deployed on a target distribution that differs from the source distributions they were trained on. Recent work in causality-based domain generalization has shown how shared causal structure between domains can induce invariant p…