What is recorded here?
Applies cooperative-game marginal contribution valuation to training data for a fixed learner and utility, with Monte Carlo, truncation and gradient-based approximation strategies.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
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.
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People and systems
Amirata Ghorbani; James Zou
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.
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English catalogue narrative
Data Shapley: learner-dependent data valuation and practical estimators
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
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.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Amirata Ghorbani; James Zou
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.