QuadraSHAP is a method for computing ε-exact Shapley values in product games, where coalition values factor across players. It replaces the exponential coalition sum with a one-dimensional polynomial integral, using Gauss–Legendre quadrature to achieve exact values when ε = 0 and provides a computable error bound for ε > 0. The approach supports weighted sums of product games, enabling baseline and empirical interventional attribution for models such as log-link regression, Cox models, odds-scale classifiers, product-kernel machines, and tree-based models, and achieves logarithmic parallel time with efficient GPU evaluation even for hundreds of thousands of features.
By Majid Mohammadi, Grigory Reznikov, Pavel Sinitcyn, Krikamol Muandet, Siu Lun Chau
arXiv:2510. 22138v3 Announce Type: replace Abstract: We show how to replace the O(2^n) coalition enumeration over n features behind Shapley values and Shapley-style interaction indices with a few-evaluation scheme on a tensor-network (TN) surrogate: TN-SHAP.
By Farzaneh Heidari, Chao Li, Guillaume Rabusseau
arXiv:2603. 03672v2 Announce Type: replace Abstract: The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space.
By Xuan Yang, Hsi-Wen Chen, Ming-Syan Chen, Jian Pei
arXiv:2607. 21094v1 Announce Type: new Abstract: We study feature-level and node-level explanations for graph neural networks (GNNs) through the lens of Aumann-Shapley attribution.
By Bizu Feng, Zhimu Yang, Shuming Wang, Shaode Yu, Yuan Cheng, Xiaojun Qian, Zixin Hu
arXiv:2602. 20376v3 Announce Type: replace-cross Abstract: We study the problem of maximizing a complex-valued quadratic form over the $K^{\text{th}}$ roots of unity.
By Ria Stevens, Fangshuo Liao, Barbara Su, Thanasis Hadjidimoulas, Jianqiang Li, Anastasios Kyrillidis
arXiv:2606. 02247v1 Announce Type: cross 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 on value function evaluations of sampled coalitions.
By David Rundel, Fabian Fumagalli, Maximilian Muschalik, Bernd Bischl, Matthias Feurer