Adaptive conformal prediction on the Grassmannian
Read the original on arXiv Machine Learning →The paper presents a conformal prediction framework for regression problems where the responses lie on the Grassmann manifold. It builds prediction regions as metric balls on the manifold, using basis‑invariant geometric distances as non‑conformity measures and scaling them with a learned prediction‑difficulty estimator to achieve locally adaptive, input‑dependent regions. The method preserves finite‑sample marginal coverage under exchangeability and is evaluated on synthetic data and an airfoil inverse shape‑design task, showing that geometry‑aware scores and local adaptation improve coverage uniformity and can reduce region size in heteroscedastic settings.
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