arXiv Machine Learning By Zhanli Wu, Fabrizio Leisen, F. Javier Rubio

Conformalized Regression for Continuous Bounded Outcomes

Read the original on arXiv Machine Learning →

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.

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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
Sep 10

Conformalized Super Learner

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 Machine Learning
Jul 1

On Optimal Data Splitting for Split Conformal Prediction

arXiv:2606. 31600v1 Announce Type: cross Abstract: Conformal prediction and its variants, including the split conformal prediction, provide a distribution-free framework for uncertainty quantification by constructing prediction intervals or sets with finite-sample coverage guarantees.

By Sayan Das, Bahram Yaghooti, Todd A. Kuffner, Soumendra N. Lahiri
arXiv Statistics ML
Sep 1

Elements of Conformal Prediction

arXiv:2603.23923v2 Announce Type: replace-cross Abstract: Predictive inference is a fundamental task in statistics, traditionally addressed using parametric assumptions about the data distribution an...

By Matteo Sesia, Stefano Favaro
arXiv Machine Learning
Sep 11

Benchmarking non-conformity score functions in conformal prediction

Conformal prediction replaces single-class predictions with prediction sets that guarantee a pre-specified coverage probability. The paper reviews properties of non‑conformity score functions, presents examples from the literature, and proposes new modifications. It introduces a method to evaluate prediction set sizes and compares different score functions, including their effectiveness for class‑conditional conformal prediction with imbalanced classes.

By Sol Erika Boman