arXiv:2410. 01917v3 Announce Type: replace-cross Abstract: Originally introduced in game theory, Shapley values have emerged as a central tool in explainable machine learning, where they are used to attribute model predictions to specific input features.
By Christopher Musco, R. Teal Witter
The paper introduces a novel method that merges Monte Carlo sampling with linear regression to estimate probabilistic values such as Shapley and Banzhaf values. It generalizes previous algorithms by allowing any efficiently computable function family, enabling the use of tree-based models like XGBoost and cross‑fitting for unbiased estimates. Experiments on eight datasets show the approach achieves state‑of‑the‑art accuracy, reducing error by up to 11× for Shapley values and 390× for general probabilistic values compared to existing methods.
By R. Teal Witter, Yurong Liu, Christopher Musco
arXiv:2608.24104v1 Announce Type: cross
Abstract: Numerous methods have been developed to quantify feature attributions in individual predictions for tree ensembles. However, many applications requir...
By Zhongli Jiang, Min Zhang, Dabao Zhang
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
arXiv:2508. 07952v2 Announce Type: replace Abstract: Clustering algorithms often assume all features contribute equally to the data structure, an assumption that usually fails in high-dimensional or noisy settings.
By Richard J. Fawley, Renato Cordeiro de Amorim
arXiv:2604. 15107v2 Announce Type: replace-cross Abstract: Shapley values provide a flexible framework for attributing feature contributions to model predictions, but they are not naturally suited for feature selection: a feature may receive a positive attribution even when it is redundant given the remaining variables.
By Chenghui Zheng, Garvesh Raskutti