Regret Minimization with Performative Feedback

Understand

In performative prediction, the deployment of a predictive model triggers a shift in the data distribution.

• As these shifts are typically unknown ahead of time, the learner needs to deploy a model to get feedback about the distribution it induces.
• We study the problem of finding near-optimal models under performativity while maintaining low regret.
• On the surface, this problem might seem equivalent to a bandit problem.