arXiv Statistics ML
Marginally Useful: An Information-Gap Identity in Conformal Prediction
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
Related stories
arXiv Machine Learning
Jul 1
Accelerating Conformal Prediction via Approximate Leave-One-Out
arXiv:2606. 31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost.
arXiv Machine Learning
2d ago
Counterfactual Online Conformal Prediction Under Adaptive Logging
The paper addresses the failure of online conformal prediction when predictions influence actions that determine which outcomes are used for calibration. It introduces Propensity-Weighted Online Conformal Prediction (PW‑OCP), an inverse‑propensity‑weighted recursion that debiases calibration, and a doubly robust variant (DR‑OCP) that further reduces bias. Experiments on synthetic decision tasks, open bandit data, and financial rebalancing demonstrate that PW‑OCP and DR‑OCP improve counterfactual coverage and downstream regret while preserving prediction‑set sharpness.
arXiv Machine Learning
Aug 19
Dynamic Regime-Aware Conformal Calibration for Reliable Economic Forecast Intervals under Multiple Distribution Shifts
Dynamic Regime-Aware Conformal Prediction (DRACP) is a new method that blends density‑ratio estimation, localized kernel weighting, and probabilistic regime‑aware weighting with a self‑tuning online significance controller to produce reliable prediction intervals under multiple distribution shifts. The authors prove finite‑sample validity with oracle weights, provide a coverage‑gap bound for estimated weights, and give deterministic or regret guarantees for the online controller. In experiments on 48 real forecasting series—including euro‑area inflation, US macroeconomic and energy indicators, and daily financial data—DRACP achieves the most reliable calibration, maintaining coverage close to the nominal 0.90 and never falling below 0.80, while other methods achieve narrower intervals but with higher under‑coverage. whyItMatters":"DRACP offers a principled trade‑off between calibration and efficiency, ensuring that prediction intervals meet coverage standards even when economic data exhibit covariate shift, concept drift, and latent regimes."
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 Statistics ML
Sep 1
Online selective conformal inference: adaptive scores, convergence rates and optimality
arXiv:2508.10336v3 Announce Type: replace-cross Abstract: In a supervised online setting, quantifying uncertainty has been proposed in the seminal work of Gibbs and Cand\`es (2021). For any given poi...
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.