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.

By Daniel Bensimon, Sean Xiang Yu, Eric D. Kolaczyk, Archer Y. Yang
arXiv Machine Learning
Aug 19

Conformal Prediction for Molecular Properties under Label Shift

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.

By Hyeonsu Lee, Juyeon Kim, Erkhembayar Jadamba, Seungjin Choi, Hyunjin Shin
arXiv Machine Learning
Jul 28

Robust Conformalized Selection with Noisy Responses

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.

By Chengyao Yu, Hongxin Wei, Bingyi Jing
arXiv Machine Learning
Oct 22

Optimized Conformal Selection: Powerful Selective Inference After Data-Dependent Score Construction

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.

By Tian Bai, Ying Jin