arXiv Machine Learning June 11, 2026 By Wenbin Zhou, Shixiang Zhu

Calibrating Decision Robustness via Inverse Conformal Risk Control

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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