English(EN) Exact and Certified Data Shapley for Weighted k-Nearest-Neighbor Regression and Soft-Label Prediction

新算法为加权KNN回归提供精确的数据Shapley值

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-15 04:00

研究人员开发了用于计算加权k近邻（KNN）回归和软标签预测中数据Shapley值的新算法。此前，在这些场景下进行精确计算在计算上是不可行的，依赖于指数级复杂度的暴力破解方法。新方法包括一种伪多项式时间精确算法和一种针对连续权重的认证全多项式时间近似方案（FPTAS），提供了确定性结果和经过认证的误差界限。这些进展对于准确评估复杂机器学习模型中训练数据点的价值至关重要，并已作为开源库发布。 AI

影响提供了精确且经过认证的数据评估方法，提高了机器学习模型的可解释性和审计性。

排序理由详细介绍机器学习中数据评估新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zongye Lyu · 2026-07-15 04:00

加权k近邻回归和软标签预测的精确且经过认证的数据Shapley

arXiv:2607.11956v1 Announce Type: cross Abstract: Data Shapley is the standard principled answer to which training points are worth what, and its k-nearest-neighbor (KNN) specialization is the version deployed in practice: the exact estimator shipped by toolkits such as pyDVL and…

本文如何被排名

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加权k近邻回归和软标签预测的精确且经过认证的数据Shapley

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