We study the problem of collaborative machine learning markets where multiple
\nparties can achieve improved performance on their machine learning tasks by
\ncombining their training data. We discuss desired properties for these machine
\nlearning markets in terms of fair revenue distribution and potential threats, including
\ndata replication. We then instantiate a collaborative market for cases where parties
\nshare a common machine learning task and where parties\u2019 tasks are different. Our
\nmarketplace incentivizes parties to submit high quality training and true validation
\ndata using a novel payment-division function that is robust-to-replication.<\/p>\n","protected":false},"excerpt":{"rendered":"
We study the problem of collaborative machine learning markets where multiple parties can achieve improved performance on their machine learning tasks by combining their training data. We discuss desired properties for these machine learning markets in terms of fair revenue distribution and potential threats, including data replication. We then instantiate a collaborative market for cases 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