arXiv:2606. 28598v1 Announce Type: cross Abstract: Prediction sets should have high coverage to be useful, but some coverage notions are more practically relevant than others.
By Aabesh Bhattacharyya, Tiffany Ding, Rina Foygel Barber
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:2602. 14913v2 Announce Type: replace Abstract: Conformal prediction (CP) offers distribution-free marginal coverage guarantees under an exchangeability assumption, but these guarantees can fail if the data distribution shifts.
By Farbod Siahkali, Ashwin Verma, Vijay Gupta
arXiv:2602. 01733v3 Announce Type: replace-cross Abstract: Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guarantees.
By Junxian Liu, Hao Zeng, Hongxin Wei
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. 00419v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may shift over time (i.
By Beepul Bharti, Ambar Pal, Jacopo Teneggi, Jeremias Sulam