New Study Expands Performative Prediction Framework with Historical Data

Global: Expanding Performative Prediction via Affine Risk Minimizers

In December 2024, the authors of a newly posted arXiv preprint (arXiv:2412.03671) introduced a set of algorithms that extend the Repeated Risk Minimization (RRM) framework by incorporating historical datasets from prior model retraining cycles. The work aims to achieve rapid convergence to a performatively stable point—where the data distribution remains unchanged after deployment—particularly in dynamic environments.

Background

Performative prediction addresses scenarios in which a model’s predictions influence the underlying data distribution, creating a feedback loop that can destabilize learning outcomes. The RRM framework has been a common approach for iteratively updating models to accommodate such shifts, typically relying on the most recent dataset snapshot.

New Algorithmic Class

The paper defines a broader class of methods termed “Affine Risk Minimizers,” which leverage multiple historical datasets rather than a single final iteration. By doing so, the authors claim the algorithms can converge to a performatively stable solution for a wider range of problem settings.

Theoretical Advances

Among the theoretical contributions, the authors present a novel upper bound applicable to methods that use only the final dataset iteration and demonstrate, for the first time, the tightness of this bound alongside previously established bounds within the same regime. Additionally, they provide the first lower‑bound analysis for RRM within the Affine Risk Minimizers class, quantifying potential speed‑up gains over traditional last‑iterate approaches.

Empirical Validation

Experimental evaluation on several benchmark tasks for performative prediction shows that incorporating historical data leads to faster convergence toward the stable point compared with standard RRM techniques. The results support the theoretical claims of improved convergence rates.

Implications

These findings suggest that historical information can be a valuable resource for stabilizing models deployed in environments where predictions affect data generation. The authors note that future work may explore additional variants within the Affine Risk Minimizer framework and assess their performance on larger‑scale real‑world applications.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.