研究人员开发了一种新的算法方法，可以有效地识别数据集中最具影响力的 SoS 数据点集合。该方法通过将搜索所有可能子集这一计算密集型任务简化为一系列 top-k 问题来简化它。该算法基于 Dinkelbach 方法，为识别这些可能显著改变统计估计和模型结论的 SoS 集合提供了一种经济高效的解决方案。 AI
影响 提供了一种更有效的方法来识别 SoS 具有影响力的 SoS 数据点，有可能提高 SoS 机器学习模型的 SoS 鲁棒性和 SoS 可解释性。
排序理由 该集群包含两篇关于 SoS 识别 SoS 具有影响力的 SoS 数据集的 SoS 统计方法的 SoS arXiv 论文。
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arXiv:2606.05919v1 Announce Type: new Abstract: Identifying most influential sets (MIS) - size-$k$ subsets whose removal maximally changes a target estimand - is typically infeasible because it requires searching over $\binom{n}{k}$ subsets. For estimands with linear-fractional l…
Identifying most influential sets (MIS) - size-$k$ subsets whose removal maximally changes a target estimand - is typically infeasible because it requires searching over $\binom{n}{k}$ subsets. For estimands with linear-fractional leave-set-out effects, we show that MIS selection…
Identifying most influential sets (MIS) - size-$k$ subsets whose removal maximally changes a target estimand - is typically infeasible because it requires searching over $\binom{n}{k}$ subsets. For estimands with linear-fractional leave-set-out effects, we show that MIS selection…
arXiv:2510.20372v4 Announce Type: replace Abstract: Small influential data subsets can dramatically impact model conclusions, with a few data points overturning key findings. While recent work identifies these most influential sets, there is no formal way to tell when maximum inf…