Two novel online conformal prediction algorithms enforce nested prediction sets across coverage levels using online optimization with regret bounds for quantile error control.
The Annals of Statistics , volume=
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A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.
CPR improves empirical coverage rate by 34% and reduces average prediction set size by 40% in KGQA benchmarks via query-level path calibration and RCVNet for discriminative nonconformity scores.
citing papers explorer
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Online Conformal Prediction: Enforcing monotonicity via Online Optimization
Two novel online conformal prediction algorithms enforce nested prediction sets across coverage levels using online optimization with regret bounds for quantile error control.
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Multi-Fidelity Quantile Regression
A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.
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Conformal Path Reasoning: Trustworthy Knowledge Graph Question Answering via Path-Level Calibration
CPR improves empirical coverage rate by 34% and reduces average prediction set size by 40% in KGQA benchmarks via query-level path calibration and RCVNet for discriminative nonconformity scores.