Presents a conformalized closed testing framework for collective outlier detection and enumeration that automatically selects suitable machine learning classifiers and two-sample tests for a given dataset.
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OCULAR applies conformal prediction to semantic perception data for local calibration of dynamics model uncertainty, yielding guaranteed prediction regions without environment-specific calibration data.
The paper proposes AQCP, an algorithm that provides asymptotic average coverage guarantees for quantum conformal prediction under arbitrary hardware noise by repeated recalibration.
RACT is a rank-adaptive permutation test for two-sample covariance matrices that targets low-rank differences via the Ky-Fan(k) norm to improve power while maintaining exact Type I error control.
The book curates and presents proofs of important existing results in conformal prediction in a unified pedagogical format with illustrations.
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Local Conformal Calibration of Dynamics Uncertainty from Semantic Images
OCULAR applies conformal prediction to semantic perception data for local calibration of dynamics model uncertainty, yielding guaranteed prediction regions without environment-specific calibration data.