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[2602.13660] Optimized Certainty Equivalent Risk-Controlling Prediction Sets
Summary
This paper presents the Optimized Certainty Equivalent Risk-Controlling Prediction Sets (OCE-RCPS), a framework designed to enhance reliability in prediction systems for safety-critical applications, addressing limitations of traditional risk-controlling methods.
Why It Matters
In high-stakes environments like medical image segmentation, ensuring the reliability of prediction systems is crucial. OCE-RCPS offers a robust approach to managing risk by providing high-probability guarantees that are essential for safety-critical applications, thus potentially improving outcomes in fields where accuracy is paramount.
Key Takeaways
- OCE-RCPS introduces a novel framework for risk management in prediction systems.
- It provides high-probability guarantees on optimized certainty equivalent risk measures.
- The framework addresses tail behavior and worst-case scenarios, crucial for safety-critical applications.
- Experiments demonstrate OCE-RCPS's effectiveness in meeting target satisfaction rates across various risk measures.
- The approach outperforms traditional methods like OCE-CRC in providing probabilistic guarantees.
Computer Science > Machine Learning arXiv:2602.13660 (cs) [Submitted on 14 Feb 2026] Title:Optimized Certainty Equivalent Risk-Controlling Prediction Sets Authors:Jiayi Huang, Amirmohammad Farzaneh, Osvaldo Simeone View a PDF of the paper titled Optimized Certainty Equivalent Risk-Controlling Prediction Sets, by Jiayi Huang and 2 other authors View PDF HTML (experimental) Abstract:In safety-critical applications such as medical image segmentation, prediction systems must provide reliability guarantees that extend beyond conventional expected loss control. While risk-controlling prediction sets (RCPS) offer probabilistic guarantees on the expected risk, they fail to capture tail behavior and worst-case scenarios that are crucial in high-stakes settings. This paper introduces optimized certainty equivalent RCPS (OCE-RCPS), a novel framework that provides high-probability guarantees on general optimized certainty equivalent (OCE) risk measures, including conditional value-at-risk (CVaR) and entropic risk. OCE-RCPS leverages upper confidence bounds to identify prediction set parameters that satisfy user-specified risk tolerance levels with provable reliability. We establish theoretical guarantees showing that OCE-RCPS satisfies the desired probabilistic constraint for loss functions such as miscoverage and false negative rate. Experiments on image segmentation demonstrate that OCE-RCPS consistently meets target satisfaction rates across various risk measures and reliability co...