We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitous, from robot planning algorithms that rely on pedestrian predictions, to calibrating autonomous manufacturing to exhibit high throughput and low error, to the choice of trusting a nominal policy versus switching to a safe backup policy at run-time. The decisions produced by our algorithms are safe in the sense that they come with provable statistical guarantees of having low risk without any assumptions on the world model whatsoever; the observations need not be I.I.D. and can even be adversarial. The theory extends results from conformal prediction to calibrate decisions directly, without requiring the construction of prediction sets. Experiments demonstrate the utility of our approach in robot motion planning around humans, automated stock trading, and robot manufacturin

翻译：我们提出共形决策理论（Conformal Decision Theory），这是一种在不依赖完美机器学习预测前提下实现安全自主决策的框架。此类决策实例无处不在：从依赖行人预测的机器人规划算法，到兼具高吞吐量与低错误率的自动化制造校准，再到运行时选择信任名义策略或切换至安全备用策略的权衡。本文算法产生的决策具备统计意义上的安全性保障——无需对世界模型作任何假设即可证明风险可控；观测数据无需独立同分布（I.I.D.），甚至可存在对抗性扰动。该理论将共形预测（Conformal Prediction）成果直接扩展至决策校准领域，无需构建预测集。实验展示了该方法在人类周围机器人运动规划、自动化股票交易及机器人制造中的实际效用。