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ShapleyOG method accelerates Shapley value estimation in machine learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ShapleyOG, a novel method for estimating Shapley values in machine learning that significantly reduces computational overhead. By focusing on the odd component of the game, ShapleyOG requires fewer observations than previous adaptive methods while maintaining estimation accuracy. This advancement makes adaptive Shapley value estimation practical for games with higher evaluation costs and larger budgets, outperforming state-of-the-art non-adaptive estimators. AI

IMPACT Accelerates the application of explainability techniques in machine learning by making Shapley value estimation more computationally feasible.

RANK_REASON This is a research 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 1 sources. How we write summaries →

ShapleyOG method accelerates Shapley value estimation in machine learning

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This is a research paper detailing a new method for estimating Shapley values in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · David Rundel, R. Teal Witter, Santo M. A. R. Thies, Matthias Feurer, Fabian Fumagalli, Bernd Bischl ·

    ShapleyOG: Fast Adaptive Shapley Value Estimation with Odd Gaussian Processes

    arXiv:2611.07173v1 Announce Type: new Abstract: Shapley values are a central tool for attribution in machine learning. Since their exact computation requires evaluating the cooperative game to be explained on exponentially many player coalitions, they are typically estimated from…

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