Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction

Abstract:We study optimization under performative prediction, where deploying a model affects the future data distribution. For this setting, several gradient-based approaches have been proposed. However, they typically assume specific data distributions or loss functions, which limit their practical applicability. To overcome these limitations, we propose a gradient-based optimization method with convergence guarantees under substantially weaker assumptions. Our method explicitly estimates the induced distribution shift through finite differences. It enables higher-dimensional optimization across broader classes of loss functions and data distributions. We also propose a practical variant that reduces the number of samples required. Numerical experiments demonstrate that our proposed algorithms converge faster and more consistently than existing ones.

Optimization and Control (math.OC); Machine Learning (cs.LG)

arXiv:2607.26562 [math.OC]

(or arXiv:2607.26562v1 [math.OC] for this version)

https://doi.org/10.48550/arXiv.2607.26562 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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