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.
The paper introduces a conformal prediction framework designed for molecular property prediction under label shift. By weighting conformal scores with marginal label probability ratios, it generates statistically rigorous prediction intervals without retraining, enabling robust uncertainty quantification when property distributions change. This approach provides actionable confidence measures that improve the reliability of AI-driven predictions in drug discovery.
arXiv:2605. 20726v2 Announce Type: replace-cross Abstract: Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-values fall below a threshold.
arXiv:2607. 22985v1 Announce Type: cross Abstract: Conformalized selection has been widely applied to select high-quality candidates from large datasets with rigorous uncertainty quantification, such as reliable labeling, drug discovery, and the alignment of large language models.
The paper introduces Optimized Conformal Selection (OptCS), a framework that enables valid multiple testing after flexible, data‑driven construction of conformity scores. OptCS allows the use of all labeled data for score training and handles complex p‑value dependencies to maintain finite‑sample false discovery rate control. Three specific procedures are proposed: selecting a powerful score among candidates, training the score with all labeled data without sample splitting, and combining full‑sample training with model selection, with effectiveness demonstrated in simulations and real drug‑discovery and language‑model applications.
arXiv:2606. 11044v1 Announce Type: cross Abstract: Conformal predictive systems (CPS) output calibrated bands of CDFs under exchangeability.
The paper introduces two new methods for conformal prediction when training data are coarsened or partially missing. Robust Split Prediction (RSP) extends doubly robust estimating equations to general coarsening patterns, producing multiply robust prediction sets. Robust Split Conformal Prediction (RSCP) combines semiparametric theory with conformal risk control by fixing a preliminary threshold estimate to restore monotonicity, enabling higher‑order bias‑controlled coverage guarantees. The authors provide theoretical results for various missingness scenarios and demonstrate the methods with simulations and a rheumatoid‑arthritis study.
arXiv:2606. 03600v1 Announce Type: cross Abstract: Standard conformal prediction (CP) procedures are typically formulated in terms of p-values, but reliance on p-values alone limits flexibility, for example, when combining dependent evidence across models or data splits.
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.
The paper introduces the Label-Shift-Adjusted Bayesian Score (LSA score), a nonconformity measure for conformal prediction that corrects Bayesian scores under label shift by applying an importance-weighted transformation of the source predictive distribution. Unlike residual-based scores that produce uniform-width intervals, the LSA score yields shorter, adaptive intervals while maintaining comparable coverage in the target domain. Experiments on molecular property prediction demonstrate that the LSA score outperforms both residual-based and source-based Bayesian scores, though all methods experience some coverage loss under stronger shifts due to density-ratio estimation challenges.
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:2507. 14023v2 Announce Type: replace-cross Abstract: Regression problems with bounded continuous outcomes frequently arise in statistical and machine learning applications, such as the analysis of rates and proportions.