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The authors identify a strategy for compensating agents to disclose private and relevant data. The strategy draws on interesting ideas from the machine learning literature, including the use of the stochastic gradient descent algorithm to set the payment for the data. This allows effective compensation of agents while maintaining a limited budget. The authors also include mechanisms for preserving the privacy of agents, and identification of different "profit maximizing" strategies for agent to select given their confidence in their data. It appears that the substantive contribution is in identifying a mechanism that compensates agents for their data while maintaing a bounded budget.