  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 where parties share a common machine learning task and where parties'
tasks are different. Our marketplace incentivizes parties to submit high
quality training and true validation data. To this end, we introduce a novel
payment division function that is robust-to-replication and customized output
models that perform well only on requested machine learning tasks. In
experiments, we validate the assumptions underlying our theoretical analysis
and show that these are approximately satisfied for commonly used machine
learning models.
