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Performative distribution shift captures the setting where the choice ofwhich ML model is deployed changes the data distribution. For example, a bankwhich uses the number of open credit lines to determine a customer's risk ofdefault on a loan may induce customers to open more credit lines in order toimprove their chances of being approved. Because of the interactions betweenthe model and data distribution, finding the optimal model parameters ischallenging. Works in this area have focused on finding stable points, whichcan be far from optimal. Here we introduce performative gradient descent(PerfGD), which is the first algorithm which provably converges to theperformatively optimal point. PerfGD explicitly captures how changes in themodel affects the data distribution and is simple to use. We support ourfindings with theory and experiments.

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