How to Learn when Data Reacts to Your Model: Performative Gradient Descent

Understand

Performative distribution shift captures the setting where the choice of which ML model is deployed changes the data distribution.

• For example, a bank which uses the number of open credit lines to determine a customer's risk of default on a loan may induce customers to open more credit lines in order to improve their chances of being approved.
• Because of the interactions between the model and data distribution, finding the optimal model parameters is challenging.
• Works in this area have focused on finding stable points, which can be far from optimal.