arXiv:2508. 13362v2 Announce Type: replace Abstract: Conformal prediction (CP) is well-suited for uncertainty quantification in time series forecasting due to its distribution-free coverage guarantees.
By Ruipu Li, Daniel Menacho, Alexander Rodr\'iguez
arXiv:2607. 26577v1 Announce Type: new Abstract: Adaptive conformal inference (ACI) of Gibbs and Cand{\`e}s and its variants are the standard approach to online conformal prediction under distribution shift, but they suffer from three fundamental limitations.
By Rahul Vaze
arXiv:2510. 07750v3 Announce Type: replace-cross Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions.
By Wenbin Zhou, Shixiang Zhu
The paper introduces a score‑calibrated robustness framework that transforms any fixed point predictor into a decision‑relevant uncertainty representation using distribution‑free conformal calibration. By employing the conformal score as the core unit of robustness, the authors derive both reliability‑based robust optimization and target‑oriented Conformal Robust Satisficing formulations, linking them through a shared robust decision frontier and a fragility measure. Experiments on synthetic data and a real online‑grocery inventory case study demonstrate the framework’s ability to improve reliability, reduce costs, and provide interpretable uncertainty scales for black‑box predictors.
By Lingjie Zhao, Hansheng Jiang, Wei Qi
arXiv:2508.10336v3 Announce Type: replace-cross
Abstract: In a supervised online setting, quantifying uncertainty has been proposed in the seminal work of Gibbs and Cand\`es (2021). For any given poi...
By Pierre Humbert, Ulysse Gazin, Ruth Heller, Etienne Roquain
arXiv:2607. 02206v1 Announce Type: cross Abstract: Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making.
By Yurui Zheng, Ying Jin