arXiv Machine Learning By Jing Li, Haibin Zhu

Sequential Confidence Sets for Coverage-Constrained Conformal Model Selection

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arXiv:2609. 28522v1 Announce Type: cross Abstract: Modern conformal forecasting systems often maintain several adaptive pipelines that differ in base forecasters, conformity scores, calibration windows, and update rules.

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arXiv Machine Learning
Jun 11

Calibrating Decision Robustness via Inverse Conformal Risk Control

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
arXiv Machine Learning
Sep 22

Conformal Robustness in Prediction-Driven Decision-Making

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