Non-asymptotic decomposition of conditional miscoverage in conformal prediction into score-estimation error, finite-sample calibration error, and intrinsic conditional-mismatch error, with guidance for model selection and extensions to covariate shift and structured data.
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8 Pith papers cite this work. Polarity classification is still indexing.
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2026 8verdicts
UNVERDICTED 8representative citing papers
Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
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.
Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
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 meta-analytic framework estimates the resilience probability of a surrogate marker to the surrogate paradox in a new study by modeling deviations from functional relationships observed in completed trials.
Temporal difference calibration aligns uncertainty estimates in vision-language-action models with their value functions for better sequential performance.
A layered differentiable optimization approach combines conformal prediction for risk-aware ellipsoids with control barrier functions and quadratic programming for safe robot navigation under sensor uncertainty.
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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.