Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm

Understand

High-quality machine learning models are dependent on access to high-quality training data.

• When the data are not already available, it is tedious and costly to obtain them.
• Data markets help with identifying valuable training data: model consumers pay to train a model, the market uses that budget to identify data and train the model (the budget allocation problem), and finally the market compensates data providers according to their data contribution (revenue allocation problem).
• For example, a bank could pay the data market to access data from other financial institutions to train a fraud detection model.