Conformal Risk Training: End-to-End Optimization of Conformal Risk Control

• URL: http://arxiv.org/abs/2510.08748v1
• Date: Thu, 09 Oct 2025 19:05:45 GMT
• Title: Conformal Risk Training: End-to-End Optimization of Conformal Risk Control
• Authors: Christopher Yeh, Nicolas Christianson, Adam Wierman, Yisong Yue,
• Abstract summary: We introduce "conformal risk training," an end-to-end approach that differentiates through conformal OCE risk control during model training or fine-tuning.<n>Our method achieves provable risk guarantees while demonstrating significantly improved average-case performance over post-hoc approaches.
• Score: 41.45834526675908
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While deep learning models often achieve high predictive accuracy, their predictions typically do not come with any provable guarantees on risk or reliability, which are critical for deployment in high-stakes applications. The framework of conformal risk control (CRC) provides a distribution-free, finite-sample method for controlling the expected value of any bounded monotone loss function and can be conveniently applied post-hoc to any pre-trained deep learning model. However, many real-world applications are sensitive to tail risks, as opposed to just expected loss. In this work, we develop a method for controlling the general class of Optimized Certainty-Equivalent (OCE) risks, a broad class of risk measures which includes as special cases the expected loss (generalizing the original CRC method) and common tail risks like the conditional value-at-risk (CVaR). Furthermore, standard post-hoc CRC can degrade average-case performance due to its lack of feedback to the model. To address this, we introduce "conformal risk training," an end-to-end approach that differentiates through conformal OCE risk control during model training or fine-tuning. Our method achieves provable risk guarantees while demonstrating significantly improved average-case performance over post-hoc approaches on applications to controlling classifiers' false negative rate and controlling financial risk in battery storage operation.

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