arXiv:2602.16015v2 Announce Type: replace Abstract: Conformal prediction gives finite-sample coverage guarantees for regression, but most standard constructions are designed for Euclidean output spaces. When the response lies on a Riemannian manifold, Euclidean residuals and coordinate-based regions can ignore the geometry that defines meaningful error. We propose adaptive geodesic conformal prediction, a simple framework that builds nonconformity scores from geodesic distances and normalizes them with a cross-validated estimate of local prediction difficulty. On the sphere, this produces geod
The continuous drive for more reliable and interpretable AI models, particularly in complex data environments, is pushing research into advanced uncertainty quantification methods, leveraging geometric insights.
Improving the accuracy and reliability of AI predictions, especially when dealing with non-Euclidean data, is crucial for deploying AI in sensitive applications and for enhancing model trustworthiness.
This research introduces a novel method for applying conformal prediction to data on Riemannian manifolds, offering more geometrically aware uncertainty guarantees than standard Euclidean approaches.
- · AI developers
- · Robotics
- · Medical imaging
- · Scientific computing
- · AI models without robust uncertainty quantification
- · Applications sensitive to prediction errors in non-Euclidean spaces
AI systems will become more reliable and trustworthy in applications involving complex or structured data.
The adoption of AI in fields like autonomous navigation and medical diagnostics where geometric constraints are critical will accelerate due to improved safety and interpretability.
New regulatory frameworks for AI might increasingly demand geometrically informed uncertainty quantification, pushing this methodology into mainstream AI deployment.
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Read at arXiv cs.LG