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Calibrating Decision Robustness via Inverse Conformal Risk Control

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arXiv:2510. 07750v3 Announce Type: replace 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.

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Authors: Wenbin Zhou, Shixiang Zhu
Released: 2026-06-11 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Calibrating Decision Robustness via Inverse Conformal Risk Control overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)". Matching terms: class, eval, rate, implement, control. Source: arxiv stat.ML (Machine Learning).
arXiv: 2510.07750

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