arXiv AI
Jul 3
Efficient Federated Conformal Prediction with Group-Conditional Guarantee
arXiv:2603. 14198v3 Announce Type: replace-cross Abstract: Deploying trustworthy AI systems requires principled uncertainty quantification.
Stochastic Grouping Conformal Prediction for Effective Subgroup Reliability
Stochastic Grouping Conformal Prediction (SGCP) is a new framework that provides distribution‑free coverage guarantees while addressing coverage disparities across clinically important subgroups. SGCP learns a stochastic grouping map so that each sample can draw calibration data from others with similar calibration behavior, creating a local score law that improves reliability for all subpopulations. The authors prove that SGCP maintains the standard coverage guarantee and demonstrate through synthetic and real‑world experiments that it reduces subgroup coverage gaps while keeping prediction set sizes small or comparable to existing methods.
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arXiv Machine Learning
Jul 21
Isotonic Conformal Prediction
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 Machine Learning
Jul 20
Improving Backward Conformal Prediction via Non-Conformity Score Transformation
arXiv:2602. 01733v3 Announce Type: replace-cross Abstract: Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guarantees.
arXiv Machine Learning
Jun 17
Questioning the Coverage-Length Metric in Conformal Prediction: When Shorter Intervals Are Not Better
arXiv:2601. 21455v2 Announce Type: replace-cross Abstract: Conformal prediction(CP) has become a cornerstone of distribution-free uncertainty quantification, conventionally evaluated by its coverage and interval length.
arXiv Machine Learning
Jun 11
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework
arXiv:2505. 08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety.
arXiv Machine Learning
Jun 4
CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support
arXiv:2605. 20468v2 Announce Type: replace Abstract: Effective medication management in Parkinson's Disease (PD) is challenging due to heterogeneous disease progression, variable patient response, and medication side effects.
arXiv Statistics ML
Oct 27
Isotonic Conformal Inference
arXiv:2607.16675v2 Announce Type: replace Abstract: A predictor with small overall bias can still exhibit systematic bias conditional on its predicted value, limiting its reliability for downstream u...
arXiv Machine Learning
Jun 16
Audited Conformal Prediction for Classification under Unknown Distribution Shift
arXiv:2606. 14909v1 Announce Type: cross Abstract: We consider the problem of uncertainty quantification for a pretrained classification model deployed under unknown distribution shift.
arXiv Machine Learning
Sep 29
Missingness-Aware Conformal Prediction Under Cross-Hospital 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 Machine Learning
Jul 15
Efficient Conformal Prediction for Regression Models under Label Noise
arXiv:2509. 15120v2 Announce Type: replace Abstract: In high-stakes scenarios, such as medical imaging applications, it is critical to equip the predictions of a regression model with reliable confidence intervals.
arXiv Machine Learning
Sep 15
Noise-Adaptive Conformal Classification with Marginal Coverage
arXiv:2501.18060v2 Announce Type: replace-cross Abstract: Conformal inference provides a rigorous statistical framework for uncertainty quantification in machine learning, enabling well-calibrated pr...
arXiv AI
Oct 28
When uncertainty quantification fails: structural vulnerabilities and corrections for selective prediction under clinical class imbalance
The paper investigates how uncertainty quantification fails in selective prediction systems when clinical data exhibit severe class imbalance, leading to systematic exclusion of minority patients. Through experiments on 56 synthetic scenarios and three neurodegenerative disease cohorts, the authors show that standard uncertainty measures become proxies for class membership rather than true ambiguity, and that combining class‑aware correction (CCR) with threshold‑aware metrics restores proper monotonicity and protects minority patients. The study proposes adopting threshold‑aligned uncertainty measures and class‑stratified evaluation to mitigate this bias and improve safety in clinical AI pipelines.