Conformal Data Contamination Tests for In-distribution Data Acquisition
The work proposes a distribution-free framework to help data agents strategically share information for model personalization. It introduces 'conformal data contamination tests,' which are new statistical procedures that reliably identify external data sources whose information might be contaminated, ensuring only valuable training data is acquired.