arXiv AI June 2, 2026 By Farzaneh Heidari, Guillaume Rabusseau

TN-SHAP-G: Graph-Structured Tensor Network Surrogates for Shapley Values and Interactions

arXiv:2606. 01540v1 Announce Type: cross Abstract: Shapley values are a widely used tool for attributing importance and interactions among input variables in black-box models, but their computation involves a function defined over an exponentially large space of subsets.

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arXiv Machine Learning

Jun 2

Tractable Shapley Values and Interactions via Tensor Networks

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 Machine Learning

Aug 4

Local Shapley: Model-Induced Locality and Optimal Reuse in Data Valuation

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 Computer Vision

Sep 15

Tensor Decomposition Structure Search Framework from an Interaction Perspective

arXiv:2603.02720v2 Announce Type: replace Abstract: Recently, tensor decompositions have attracted increasing attention. Fundamentally, different interactions among factors induce distinct tensor dec...

By Ting-Wei Zhou, Xi-Le Zhao, Sheng Liu, Wei-Hao Wu, Yu-Bang Zheng, Deyu Meng

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arXiv AI

Sep 1

Hyper-Fold: Exploring the Expressive Limit of Sequence-Geometry Learning for Proteins via Hypergraph Modeling

arXiv:2608.29207v1 Announce Type: new Abstract: Protein structure modeling rests on a single computational primitive: the interaction between what a residue is (sequence content) and where it sits (t...

By Yifan Feng, Guanjie Cheng, Shihui Ying, Shaoyi Du, Yue Gao

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arXiv Machine Learning

Jun 2

Beyond Additive Decompositions: Interpretability Through Separability

arXiv:2605. 31200v2 Announce Type: replace Abstract: Interpretable machine learning requires models that are accurate and structurally faithful to the data.

By Jinyang Liu, Munir Eberhardt Hiabu

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arXiv Machine Learning

Aug 13

RelShap: Relationally Consistent Shapley Explanations

arXiv:2608. 11508v1 Announce Type: new Abstract: Machine learning pipelines commonly flatten relational data into single-table representations, discarding structural constraints.

By Seungeun Lee, Joao Fonseca, Julia Stoyanovich

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Related stories

Tractable Shapley Values and Interactions via Tensor Networks

Local Shapley: Model-Induced Locality and Optimal Reuse in Data Valuation

Tensor Decomposition Structure Search Framework from an Interaction Perspective

Hyper-Fold: Exploring the Expressive Limit of Sequence-Geometry Learning for Proteins via Hypergraph Modeling

Beyond Additive Decompositions: Interpretability Through Separability

RelShap: Relationally Consistent Shapley Explanations