Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions

arXiv:2310.05921v3 Announce Type: replace-cross
Abstract: 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 …

abstract algorithms arxiv autonomous cs.lg cs.ro decision decisions error examples framework low machine machine learning manufacturing pedestrian planning predictions robot safe stat.me stat.ml theory type