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 manufacturing.

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的前提下实现安全自主决策的框架体系。此类决策场景广泛存在，从依赖行人预测的机器人规划算法，到实现高吞吐低误差的自动化制造校准，再到运行时选择信任标称策略还是切换至安全备份策略的决策。我们的算法所生成的决策具备统计安全性保证——无论世界模型假设如何，即使在观测值不满足独立同分布甚至具有对抗性的情况下，仍能给出低风险的严格统计保障。该理论将共形预测（Conformal Prediction）的结论直接推广至决策校准阶段，无需构建预测集。实验验证了该方法在人群环境机器人运动规划、自动化股票交易及机器人制造领域的实用价值。