研究人员开发了用于近似 Shapley 值的新方法，Shapley 值是机器学习中归因的关键指标。两篇论文介绍了新算法 Adalina 和 ShaplEIG，它们提高了估算这些值（特别是对于大量“参与者”或特征）的效率和准确性。另一篇论文 OddSHAP 为配对采样技术提供了理论依据，并引入了一种利用这一见解实现最先进准确度的新估计器。 AI
影响 Shapley 值近似方面的这些进步可能导致在复杂的机器学习模型中实现更高效、更准确的归因，从而提高可解释性和信任度。
排序理由 多篇学术论文在 arXiv 上发表，详细介绍了近似 Shapley 值的新方法和理论依据。
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arXiv:2604.08438v2 Announce Type: replace Abstract: The Shapley value, and its broader family of semi-values, has received much attention in various attribution problems. A fundamental and long-standing challenge is their efficient approximation, since exact computation generally…
arXiv:2606.02247v1 Announce Type: new Abstract: Shapley values are a principled attribution measure widely used in interpretable machine learning, but their exact computation scales exponentially with the number of players, motivating a wide range of approximation methods based o…
Shapley values are a principled attribution measure widely used in interpretable machine learning, but their exact computation scales exponentially with the number of players, motivating a wide range of approximation methods based on value function evaluations of sampled coalitio…
arXiv:2602.01399v2 Announce Type: replace-cross Abstract: The Shapley value is a ubiquitous framework for attribution in machine learning, encompassing feature importance, data valuation, and causal inference. However, its exact computation is generally intractable, necessitating…