We extend conformal prediction to control the expected value of any monotone loss function. The algorithm generalizes split conformal prediction together with its coverage guarantee. Like conformal prediction, the conformal risk control procedure is tight up to an $\mathcal{O}(1/n)$ factor. We also introduce extensions of the idea to distribution shift, quantile risk control, multiple and adversarial risk control, and expectations of U-statistics. Worked examples from computer vision and natural language processing demonstrate the usage of our algorithm to bound the false negative rate, graph distance, and token-level F1-score.

翻译：本文将保形预测方法推广至控制任意单调损失函数的期望值。该算法推广了分割保形预测及其覆盖保证。与保形预测类似，保形风险控制过程在$\mathcal{O}(1/n)$因子内具有紧致性。我们进一步引入该方法的扩展形式，涵盖分布偏移、分位数风险控制、多重及对抗性风险控制，以及U-统计量的期望控制。来自计算机视觉和自然语言处理领域的实证案例展示了该算法在界定额外负率、图距离以及词元级F1得分方面的实际应用。