arXiv Machine Learning June 4, 2026 By Ricardo Diaz-Rincon, Muxuan Liang, Adolfo Ramirez-Zamora, Benjamin Shickel

CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support

Read the original on arXiv Machine Learning →

arXiv:2605. 20468v2 Announce Type: replace Abstract: Effective medication management in Parkinson's Disease (PD) is challenging due to heterogeneous disease progression, variable patient response, and medication side effects.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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

Jul 15

Efficient Conformal Prediction for Regression Models under Label Noise

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

arXiv Machine Learning

Jun 11

PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework

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

computer-vision benchmarks safety

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.

By Yizhou Min, Yizhou Lu, Lanqi Li, Zhen Zhang, Jiaye Teng

arXiv AI

Jun 24

Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning

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

llms

arXiv Machine Learning

Jul 20

A Transportable Threshold-Based Framework for Interpretable Classification of Medical Data

arXiv:2607. 15394v1 Announce Type: new Abstract: Black-box models limit the adoption of artificial intelligence in medicine due to their lack of interpretability and reproducibility.

By Antony Garcia, Adrian Noriega, Gabrielle Britton, Xinming Huang

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