arXiv:2510.12249v2 Announce Type: replace Abstract: In performative learning, the data distribution reacts to the deployed model - for example, because strategic users adapt their features to game it - which creates a more complex dynamic than in classical supervised learning. One should thus not only optimize the model for the current data but also take into account that the model might steer the distribution in a new direction, without knowing the exact nature of the potential shift. We explore how regularization can help cope with performative effects by studying its impact in high-dimensio
This paper addresses an increasingly critical challenge in AI as deployed models interact with and influence real-world data distributions, leading to complex feedback loops.
Understanding optimal regularization in performative learning is crucial for developing robust and stable AI systems, preventing unintended consequences and ensuring reliable model performance over time.
This research provides theoretical groundwork that could lead to more stable and predictable AI deployments in dynamic environments, moving beyond classical supervised learning assumptions.
- · AI researchers
- · Developers of adaptive AI systems
- · Industries deploying AI in strategic user environments
- · Developers neglecting performative effects
- · Systems unprepared for data distribution shifts
Improved stability and predictability of AI models operating in dynamic, user-influenced environments.
Accelerated development of more sophisticated AI agents capable of learning and adapting while accounting for their own impact.
Enhanced trust in AI systems due to their increased robustness against strategic user behavior and data shifts.
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