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.
SA-BCP adaptively blends temporal Bayesian predictions with spatial KDE evidence via threshold K, derives closed-form MSE-optimal K, and provides an online selection procedure with regret bounds, yielding sharper intervals at nominal coverage on volatility and weather data.
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 for conditional quantiles that represents the high-fidelity quantile as a low-fidelity quantile evaluated at a covariate-dependent level, with theory on faster convergence rates under shape similarity.
CPR uses query-level conformal calibration over path scores and a PUCT-trained RCVNet to achieve valid coverage guarantees and smaller prediction sets in KGQA, reporting 45% higher empirical coverage and 52% smaller sets than prior conformal baselines.
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.
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.
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Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction
SA-BCP adaptively blends temporal Bayesian predictions with spatial KDE evidence via threshold K, derives closed-form MSE-optimal K, and provides an online selection procedure with regret bounds, yielding sharper intervals at nominal coverage on volatility and weather data.
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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.