arXiv:2604.21595v1 Announce Type: new Abstract: Multivariate conformal prediction requires nonconformity scores that compress residual vectors into scalars while preserving certain implicit geometric structure of the residual distribution. We introduce a Multivariate Kernel Score (MKS) that produces prediction regions that explicitly adapt to this geometry. We show that the proposed score resemble
A Kernel Nonconformity Score for Multivariate Conformal Prediction
Louis Meyer, Wenkai Xu·arXiv stat.ML··1 min read
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