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New OddSHAP method improves Shapley value estimation in ML

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called OddSHAP for estimating Shapley values, a crucial tool for machine learning attribution. This novel approach focuses on the 'odd component' of set functions, which has been identified as the sole determinant of Shapley values. By filtering out the irrelevant 'even component' through a technique called paired sampling, OddSHAP achieves state-of-the-art accuracy, particularly at larger sampling budgets. AI

IMPACT Introduces a more accurate method for model interpretability and feature attribution.

RANK_REASON The cluster contains an academic paper detailing a new method for estimating Shapley values in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · David Rundel, Fabian Fumagalli, Maximilian Muschalik, Bernd Bischl, Matthias Feurer ·

    ShaplEIG: Bayesian Experimental Design for Shapley Value Estimation

    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…

  2. arXiv stat.ML TIER_1 English(EN) · Fabian Fumagalli, Landon Butler, Justin Singh Kang, Kannan Ramchandran, R. Teal Witter ·

    An Odd Estimator for Shapley Values

    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…

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