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New methods offer provably accurate Shapley value estimation for AI explainability · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two new arXiv papers introduce advanced methods for estimating Shapley values, a key technique in explainable AI for attributing model predictions to input features. The first paper, "Provably Accurate Shapley Value Estimation via Leverage Score Sampling," presents Leverage SHAP, a modification of Kernel SHAP that requires significantly fewer model evaluations and shows a 50% error reduction. The second paper, "Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values," combines Monte Carlo sampling with regression techniques, achieving state-of-the-art performance with error rates substantially lower than existing methods, including Leverage SHAP. AI

IMPACT These advancements in Shapley value estimation could lead to more efficient and accurate AI explainability, improving trust and debugging in complex models.

RANK_REASON Two arXiv papers introducing novel algorithms for Shapley value estimation.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New methods offer provably accurate Shapley value estimation for AI explainability · 2 sources tracked

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Christopher Musco, R. Teal Witter ·

    Provably Accurate Shapley Value Estimation via Leverage Score Sampling

    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. However, computing Shapley…

  2. arXiv cs.AI TIER_1 English(EN) · R. Teal Witter, Yurong Liu, Christopher Musco ·

    Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values

    arXiv:2506.11849v3 Announce Type: replace-cross Abstract: With origins in game theory, probabilistic values like Shapley values, Banzhaf values, and semi-values have emerged as a central tool in explainable AI. They are used for feature attribution, data attribution, data valuati…

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