Performative Prediction with Selective Labels

Key points

• Performative prediction studies this interaction through a distribution map that relates each model to the population distribution it induces.1
• One of the main results in this framework showed that repeated risk minimization (RRM), which updates models by retraining on the most recent data, can converge to a stable model that minimizes risk on its own induced distribution.1
• In this work, we formalize performative prediction with selective labels and show that retraining only on observed data can misguide the retraining procedure and undermine the guarantees of convergence to a stable solution.1
• Experiments in a lending application with fairness regularization show that our robust optimization approach closely matches the performance of RRM with complete label access.1

Sources (1)

• [1]Performative Prediction with Selective Labels
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 12:45 PM

  Many social applications of machine learning exhibit performative effects: population behavior changes in response to deployed models.

  Performative prediction studies this interaction through a distribution map that relates each model to the population distribution it induces.

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