arXiv:2610.10168v1 Announce Type: cross Abstract: Split conformal prediction uses prediction errors on held-out (calibration) data to determine how wide the prediction intervals should be. It guarant...
Conformal Calibration Transfer addresses the challenge of applying conformal prediction when labeled calibration data is only available in a source space, while predictions are needed in a target space linked via unlabeled paired observations. The proposed Transported Conformal Calibration (TCC) method transports source calibration into the target domain and then corrects residual mismatches using only unlabeled target inputs, with two variants: TCC‑KS, which conservatively adjusts calibration based on a label‑free uncertainty surrogate, and weighted‑TCC, which reweights transported calibration for efficiency when weights are stable. Finite‑sample target‑domain coverage guarantees are provided, and experiments on CIFAR‑100‑C, Tiny‑ImageNet‑C, and SEN12MS demonstrate reliable coverage transfer without labeled target data, along with label‑free diagnostics to signal when correction is required.
arXiv:2609.30886v1 Announce Type: new Abstract: Conformal prediction can lose coverage when the data distribution changes after deployment. We study adaptation using labeled source data and unlabeled...
arXiv:2607. 16675v1 Announce Type: cross Abstract: A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use in downstream decision-making.
arXiv:2608. 00701v1 Announce Type: cross Abstract: Reweighting source samples to match a target covariate distribution is a standard response to distribution shift when generalizing evidence from one population to another.
arXiv:2508. 13362v2 Announce Type: replace Abstract: Conformal prediction (CP) is well-suited for uncertainty quantification in time series forecasting due to its distribution-free coverage guarantees.
arXiv:2507. 20975v5 Announce Type: replace-cross Abstract: Operator models are regression algorithms between Banach spaces of functions.
arXiv:2606. 14909v1 Announce Type: cross Abstract: We consider the problem of uncertainty quantification for a pretrained classification model deployed under unknown distribution shift.
The paper introduces a missingness‑aware conformal calibration method for mortality prediction that accounts for cross‑hospital distribution shifts. By selecting a measurement on an independent sample, grouping patients by whether that measurement is recorded, and applying Mondrian calibration within each group, the method avoids reusing calibration outcomes. Experiments on eICU and MIMIC‑IV data show that, compared to pooled calibration, it reduces the worst‑group coverage gap by a median of 1.9 percentage points across six settings, though the benefit varies with predictor and hospital.
arXiv:2608.24665v1 Announce Type: new Abstract: Power outage prediction models are increasingly used in assessments of climate-driven infrastructure risk, yet current evaluation practices obscure whe...
arXiv:2609.14531v1 Announce Type: cross Abstract: Many modern prediction tasks involve data from multiple heterogeneous sources, while the test distribution may differ substantially from any individu...
arXiv:2608. 19383v1 Announce Type: cross Abstract: Average dose-response functions are widely used to summarize causal effects of continuous treatments, but most existing methods assume that the observed sample represents the target population.