arXiv Machine Learning
Jul 10

Multi-Distribution Robust Conformal Prediction

arXiv:2601. 02998v2 Announce Type: replace Abstract: In many fairness and distribution robustness problems, one has access to labeled data from multiple source distributions yet the test data may come from an arbitrary member or a mixture of them.

By Yuqi Yang, Ying Jin
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
Aug 7

Beyond Marginal Validity: Finite-Sample Guarantees for Localized Conformal Prediction

arXiv:2608. 06206v1 Announce Type: cross Abstract: Conformal prediction endows arbitrary black-box predictors with finite-sample, distribution-free marginal coverage, yet marginal validity can hide severe covariate-specific miscalibration, while exact distribution-free conditional coverage is finite-sample unattainable.

By Anton Conrad, Rustam Isaev, Denis Belomestny, Eric Moulines, Sergey Samsonov