AWhat changed
Applies cooperative-game marginal contribution valuation to training data for a fixed learner and utility, with Monte Carlo, truncation and gradient-based approximation strategies.
Scientific scope
Applies cooperative-game marginal contribution valuation to training data for a fixed learner and utility, with Monte Carlo, truncation and gradient-based approximation strategies. Inherits the Shapley uniqueness result rather than inventing it; values depend on dataset, learner and utility, not universal monetary value. Exact computation is exponential; truncation and gradient shortcuts are not exact unbiased Shapley values. Only general ML/image/synthetic scope is curated, excluding biomedical applications.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits the Shapley uniqueness result rather than inventing it; values depend on dataset, learner and utility, not universal monetary value. Exact computation is exponential; truncation and gradient shortcuts are not exact unbiased Shapley values. Only general ML/image/synthetic scope is curated, excluding biomedical applications.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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