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: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.
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...
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:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).
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:2606. 15217v1 Announce Type: cross Abstract: Offline model-based optimization (MBO) proposes candidates by optimizing a surrogate trained on a fixed historical dataset.
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:2607. 06605v1 Announce Type: new Abstract: Conformal prediction is being adopted in drug discovery to put an honest number on model reliability: pick an error rate alpha, and the method returns prediction sets containing the true label with probability at least 1 - alpha.
arXiv:2608. 19906v1 Announce Type: new Abstract: Accurately ranking active ligands for a target protein pocket from massive chemical libraries remains a central challenge in virtual screening.
arXiv:2606. 11865v1 Announce Type: cross Abstract: Conformal Bayes combines Bayesian posterior predictives with conformal calibration to produce prediction sets that are both statistically valid and geometrically efficient.
arXiv:2509. 20345v3 Announce Type: replace-cross Abstract: The rapid proliferation of high-quality synthetic data -- generated by advanced AI models or collected as auxiliary data from related tasks -- presents both opportunities and challenges for statistical inference.
arXiv:2604.22391v2 Announce Type: replace-cross Abstract: The Super Learner (SL) is a widely used ensemble method that combines point predictions from a library of learners based on their predictive...