Adaptive conformal prediction on the Grassmannian
We introduce a conformal prediction framework for regression with Grassmann-valued responses. The proposed method constructs prediction regions in the form of metric balls directly on the Grassmann manifold, using basis-invariant geometric distances as non-conformity measures and scaling the corresponding non-conformity scores through a learned prediction difficulty estimator. This yields locally adaptive, input-dependent prediction regions that respect the underlying manifold geometry, while retaining the finite-sample marginal coverage guarantee of split conformal prediction under exchangeability. We evaluate several Grassmannian distance metrics and compare their prediction-region effi...
| Authors | Dimitrios Loukrezis, Zachary J. Grey, Dimitris G. Giovanis |
|---|---|
| Primary category | stat.ML |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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