The paper introduces second-score procurement mechanisms for data markets that achieve truthful cost reporting and approximately truthful quality reporting via ex-post statistical verification, with misreporting deviations vanishing as sample size grows.
Lava: Data valuation without pre-specified learning algorithms,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
citing papers explorer
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Buying Data of Unknown Quality: Fisher Information Procurement Auctions
The paper introduces second-score procurement mechanisms for data markets that achieve truthful cost reporting and approximately truthful quality reporting via ex-post statistical verification, with misreporting deviations vanishing as sample size grows.
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Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.