Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction
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 re...
| Authors | Hiroki Hamaguchi, Yuya Hikima, Hiroshi Sawada et al. |
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| Primary category | math.OC |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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