Conformal Risk Control

Conformal risk control (CRC) is a post-processing technique enhancing the reliability of machine learning models by providing calibrated uncertainty estimates, guaranteeing control over the expected value of a chosen loss function. Current research focuses on extending CRC to diverse applications, including multi-agent systems, sensor networks, and various prediction tasks (e.g., classification, regression, and sequence prediction), often employing algorithms based on conformal prediction, online learning, and convex optimization. This framework offers valuable tools for ensuring trustworthy model performance in high-stakes scenarios, improving decision-making in fields ranging from robotics and healthcare to environmental monitoring and natural language processing. The distribution-free nature of many CRC methods makes them particularly attractive for applications with limited data or unknown underlying distributions.