Collaborative Machine Learning Markets - Ohrimenko, Olga; Tople, Shruti; Tschiatschek, Sebastian (Smooth Games Optimization (NeurIPS Workshop), 2019)
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 using a novel payment-division function that is robust-to-replication.