A mechanism using semivalues and unknown validation sets provably ensures collaborative fairness and truthfulness at equilibrium for Bayesian models.
Mathematics of Operations Research , volume=
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NASH decomposes the validation utility into Shapley-informative component functions and aggregates them non-linearly to make Data Shapley-based data selection consistently effective.
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Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning
A mechanism using semivalues and unknown validation sets provably ensures collaborative fairness and truthfulness at equilibrium for Bayesian models.
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Is Data Shapley Not Better than Random in Data Selection? Ask NASH
NASH decomposes the validation utility into Shapley-informative component functions and aggregates them non-linearly to make Data Shapley-based data selection consistently effective.