arXiv:2602.14913v2 Announce Type: replace Abstract: Conformal prediction (CP) offers distribution-free marginal coverage guarantees under an exchangeability assumption, but these guarantees can fail if the data distribution shifts. We analyze the use of pseudo-calibration as a tool to counter this performance loss under a bounded label-conditional covariate shift model. Using tools from domain adaptation, we derive a lower bound on target coverage in terms of the source-domain loss of the classifier and a Wasserstein measure of the shift. Using this result, we provide a method to design pseudo
The increasing deployment of AI models in dynamic real-world environments necessitates robust methods for maintaining performance under distribution shifts, a core challenge for AI reliability.
This research provides a theoretical framework and method to ensure AI model reliability and trustworthiness in environments where data drift is common, which is crucial for critical applications.
The ability to provide coverage guarantees for AI predictions even when the underlying data distribution shifts improves the practical applicability and safety of AI systems, particularly in sensitive sectors.
- · AI Safety Researchers
- · Autonomous Systems Developers
- · Healthcare AI Providers
- · Financial AI Systems
- · AI Models Lacking Robustness
- · Purely Data-Centric AI Approaches
- · Companies Deploying Naive AI Models
Increased trustworthiness and broader adoption of AI in high-stakes environments due to improved reliability under shifting conditions.
Development of new industry standards and regulatory requirements for AI model performance under distribution shift.
Acceleration of research into more advanced adaptive AI systems that can continuously learn and recalibrate in real-time dynamic environments.
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