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预算约束多源反事实标注离策略评估研究

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2026年10月9日,arXiv Machine Learning Theory(一手)发表研究,提出在预算约束下为 contextual-bandit 离策略评估(OPE)获取反事实标注的方法。该方法按各标注源的成本与误差特征,在上下文-动作对之间做整数分配,以降低估计方差中依赖标注计划的部分。目前公开信息仅涉及该方法框架,未见实验数据或后续验证报道。

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Oct 9, 2026
  1. arXiv · Machine Learning Theory
    预算约束下的多源反事实标注用于离策略评估

    该研究提出在预算约束下为 contextual-bandit 离策略评估(OPE)获取反事实标注的方法,按各标注源的成本与误差特征在上下文-动作对间做整数分配,以降低估计方差中依赖标注计划的部分。

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