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: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
The paper investigates how pre‑training strategy, dataset size, and domain affect uncertainty estimation in vision medical foundation models. It compares point‑prediction calibration with conformal (region) prediction across retinal, histopathological, and chest X‑ray models, finding that domain‑specific, self‑supervised pre‑training yields better calibration and more efficient conformal sets. The study shows that standard recalibration alone cannot fully reconcile uncertainty differences between models trained on different data sources.
By Haoxu Huang, Narges Razavian
The paper introduces egRUE, an explainable uncertainty estimation method that merges uncertainty quantification with feature‑level explanations for medical AI predictions. egRUE incorporates prediction explanations into its uncertainty calculation and decomposes uncertainty into contributions from individual features. Experiments and a user study with medical experts show that egRUE improves reliability, interpretability, and calibrated trust compared to existing methods.
By Li Rong Wang, Jamie Duell, Xinran Xu, Thomas C. Henderson, Yu Yue Hew, Pik Wan Erica Chiang, Xiao Wei Alstar Ang, Bingwen Eugene Fan, Xiuyi Fan
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...
By Zhanli Wu, Fabrizio Leisen, Miguel-Angel Luque-Fernandez, F. Javier Rubio
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.
By Yahav Cohen, Jacob Goldberger, Tom Tirer
The paper introduces a score‑calibrated robustness framework that transforms any fixed point predictor into a decision‑relevant uncertainty representation using distribution‑free conformal calibration. By employing the conformal score as the core unit of robustness, the authors derive both reliability‑based robust optimization and target‑oriented Conformal Robust Satisficing formulations, linking them through a shared robust decision frontier and a fragility measure. Experiments on synthetic data and a real online‑grocery inventory case study demonstrate the framework’s ability to improve reliability, reduce costs, and provide interpretable uncertainty scales for black‑box predictors.
By Lingjie Zhao, Hansheng Jiang, Wei Qi
arXiv:2609.10333v1 Announce Type: new
Abstract: Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-fr...
By Xuan Cuong Ngo, Ngan Le
arXiv:2505. 08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety.
By Abhineet Agarwal, Fange Xiao, Rebecca Barter, Omer Ronen, Boyu Fan, Bin Yu
In high-stakes healthcare applications, machine learning models are frequently trained on data from one patient population and deployed on another, creating a distribution shift that degrades both acc...
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.
By Yizhou Min, Yizhou Lu, Lanqi Li, Zhen Zhang, Jiaye Teng
arXiv:2606. 24604v1 Announce Type: new Abstract: Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is.
By Arya Hariharan, Shreyank N Gowda, Anala M R