Optimizing the Performative Risk under Weak Convexity Assumptions. (arXiv:2209.00771v4 [cs.LG] UPDATED)

In performative prediction, a predictive model impacts the distribution that
generates future data, a phenomenon that is being ignored in classical
supervised learning. In this closed-loop setting, the natural measure of
performance named performative risk ($\mathrm{PR}$), captures the expected loss
incurred by a predictive model \emph{after} deployment. The core difficulty of
using the performative risk as an optimization objective is that the data
distribution itself depends on the model parameters. This dependence is
governed by the environment and not under …

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