arXiv Machine Learning By Yahav Cohen, Jacob Goldberger, Tom Tirer

Efficient Conformal Prediction for Regression Models under Label Noise

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Sep 15

Noise-Adaptive Conformal Classification with Marginal Coverage

arXiv:2501.18060v2 Announce Type: replace-cross Abstract: Conformal inference provides a rigorous statistical framework for uncertainty quantification in machine learning, enabling well-calibrated pr...

By Teresa Bortolotti, Y. X. Rachel Wang, Xin Tong, Alessandra Menafoglio, Simone Vantini, Matteo Sesia
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 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
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