A decomposition-based modular conformal prediction method for two-stage models with FWER-controlled stage-wise scaling and adaptive extension for non-stationary data.
Conformal prediction beyond exchangeability.The Annals of Statistics, 51(2):816–845
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An online conformal prediction framework for non-exchangeable panel data that forms prediction sets using related units' contemporaneous data with adaptive similarity weights and miscoverage levels to deliver stepwise and long-run coverage guarantees.
OLCP and OLCP-Hedge achieve long-run valid coverage in non-exchangeable online settings with narrower prediction sets by localizing conformal prediction to covariates and selecting bandwidth via online convex optimization.
Branched Normalizing Flow improves conditional coverage robustness of conformal prediction under distribution shift by normalizing test inputs to the calibration distribution and mapping prediction sets back.
A new kernel nonconformity score for multivariate conformal prediction that adapts to residual geometry, provides finite-sample coverage, and achieves convergence rates based on effective kernel rank rather than ambient dimension.
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Decomposition-Based Modular Conformal Prediction for Two-Stage Modeling
A decomposition-based modular conformal prediction method for two-stage models with FWER-controlled stage-wise scaling and adaptive extension for non-stationary data.
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Online Conformal Prediction for Non-Exchangeable Panel Data
An online conformal prediction framework for non-exchangeable panel data that forms prediction sets using related units' contemporaneous data with adaptive similarity weights and miscoverage levels to deliver stepwise and long-run coverage guarantees.
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Online Localized Conformal Prediction
OLCP and OLCP-Hedge achieve long-run valid coverage in non-exchangeable online settings with narrower prediction sets by localizing conformal prediction to covariates and selecting bandwidth via online convex optimization.
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Robust Conditional Conformal Prediction via Branched Normalizing Flow
Branched Normalizing Flow improves conditional coverage robustness of conformal prediction under distribution shift by normalizing test inputs to the calibration distribution and mapping prediction sets back.
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A Kernel Nonconformity Score for Multivariate Conformal Prediction
A new kernel nonconformity score for multivariate conformal prediction that adapts to residual geometry, provides finite-sample coverage, and achieves convergence rates based on effective kernel rank rather than ambient dimension.