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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Trimming helps conformal prediction under contamination precisely when the anomaly score separates retention probabilities without biasing clean scores, otherwise the retained mixture coefficient prevents substantial decontamination.
A classification-integrated conformal framework for zero-inflated outcomes that guarantees marginal coverage and asymptotic minimal length under exchangeability, independent of the underlying models.
C-SymmPI reformulates conditional coverage as miscoverage error over a user-specified function class to deliver near-conditional guarantees under group symmetries and distributional invariance.
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.
Clipped least-squares importance fitting enables weighted conformal prediction to achieve dataset-conditional coverage guarantees under unbounded covariate shifts by bounding undercoverage and estimating a corrective inflation factor from data.
SA-BCP achieves optimal spatio-temporal decoupling in Bayesian conformal prediction by gating temporal inertia with spatial kernel-density evidence, governed by a minimax bias-variance threshold K, and outperforms ACI and Bayesian CP baselines on financial datasets.
The weighted Holm procedure (WHP) based on ordered weighted p-values is uniformly more powerful than the weighted alternative Holm procedure (WAP) based on ordered raw p-values, with stronger optimality properties under FWER control.
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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When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination
Trimming helps conformal prediction under contamination precisely when the anomaly score separates retention probabilities without biasing clean scores, otherwise the retained mixture coefficient prevents substantial decontamination.
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Classification-Powered Conformal Inference for Zero-inflated Outcomes
A classification-integrated conformal framework for zero-inflated outcomes that guarantees marginal coverage and asymptotic minimal length under exchangeability, independent of the underlying models.
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Conditional Predictive Inference for General Structured Data with Group Symmetries
C-SymmPI reformulates conditional coverage as miscoverage error over a user-specified function class to deliver near-conditional guarantees under group symmetries and distributional invariance.
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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.
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Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
Clipped least-squares importance fitting enables weighted conformal prediction to achieve dataset-conditional coverage guarantees under unbounded covariate shifts by bounding undercoverage and estimating a corrective inflation factor from data.
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Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction
SA-BCP achieves optimal spatio-temporal decoupling in Bayesian conformal prediction by gating temporal inertia with spatial kernel-density evidence, governed by a minimax bias-variance threshold K, and outperforms ACI and Bayesian CP baselines on financial datasets.
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Weighted Holm Procedures: Theory, Properties, and Recommendations
The weighted Holm procedure (WHP) based on ordered weighted p-values is uniformly more powerful than the weighted alternative Holm procedure (WAP) based on ordered raw p-values, with stronger optimality properties under FWER control.