发表机构
Chiba University; National Institute of Informatics; University of Southern Denmark（千叶大学; 国立情报学研究所; 南丹麦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出熵最小核（ELC）方法，通过平滑近似和温度路径高效计算最小核归因，在上下文干扰者博弈中实现快速、准确的危害识别与排序。
AI 中文摘要
博弈论归因通过将信用分配给特征或训练样本来解释模型。最小核作为Shapley式平均的替代方案引起了关注，因为它能够揭示在罕见、高价值上下文中造成重大损害的参与者。然而，最小核分配通常是非唯一的，分配的选择会影响最终的解释。在本研究中，我们探讨了收益选择和联盟采样如何影响最小核归因。我们的实验表明，选择器的选择对于区分有益和有害贡献至关重要，并且采样会降低测试选择器对有害参与者的识别能力，即使有益参与者仍被良好识别。这些观察结果促使我们使用所有联盟约束和定义明确的选择器进行高效计算。我们引入了熵最小核（ELC），一种平滑近似，其唯一最小化器沿着温度连续路径趋向核仁，核仁是最小核的经典细化。我们的实验表明，ELC比基于LP的核仁求解器更快地逼近核仁，同时保持较小的收益误差，并且在更大问题规模下具有进一步的GPU加速。在测试的全联盟上下文干扰者博弈中，ELC在识别准确性上匹配最小范数选择器，并且更准确地按危害程度对干扰者进行排序。
英文摘要
Game-theoretic attribution explains a model by assigning credit to its features or training examples. The least core has attracted interest as an alternative to Shapley-style averaging because it can expose players that cause substantial harm in rare, high-value contexts. However, least-core allocations are generally nonunique, and the choice of allocation can affect the resulting explanation. In this study, we investigate how payoff selection and coalition sampling affect least-core attribution. Our experiments show that selector choice matters for distinguishing useful and harmful contributions, and that sampling can degrade harmful-player identification across the tested selectors even when useful players remain well identified. These observations motivate efficient computation with all coalition constraints and a well-defined selector. We introduce entropic least core (ELC), a smooth approximation whose unique minimizer follows a continuous path along the temperature to the nucleolus, a classical refinement of the least core. Our experiments show that ELC approximates the nucleolus faster than an LP-based nucleolus solver while retaining small payoff errors, with further GPU acceleration at larger problem sizes. In the tested full-coalition contextual-distractor games, ELC matches the minimum-norm selector in identification accuracy and more accurately ranks distractors by harm.
Comments12 + 19 pages, 0 + 7 figures, 5 + 8 tables
