OCULAR calibrates dynamics uncertainty using perception from similar environments to give guaranteed prediction regions for unseen test conditions.
APACrefauthors \ 2020
5 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
citing papers explorer
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Local Conformal Calibration of Dynamics Uncertainty from Semantic Images
OCULAR calibrates dynamics uncertainty using perception from similar environments to give guaranteed prediction regions for unseen test conditions.
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Collective Outlier Detection and Enumeration with Conformalized Closed Testing
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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Adaptive Conformal Prediction for Quantum Machine Learning
The paper proposes AQCP, an algorithm that provides asymptotic average coverage guarantees for quantum conformal prediction under arbitrary hardware noise by repeated recalibration.
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Rank-adaptive covariance testing with applications to genomics and neuroimaging
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.
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Theoretical Foundations of Conformal Prediction
The book curates and presents proofs of important existing results in conformal prediction in a unified pedagogical format with illustrations.