Conformal Method

Conformal methods provide a powerful framework for quantifying uncertainty in predictions, offering finite-sample validity guarantees under minimal assumptions about the data distribution. Current research focuses on extending conformal prediction to non-exchangeable data, such as time series, and adapting it for various tasks including parameter estimation, anomaly detection, and even mitigating large language model hallucinations. This robust approach is increasingly significant for building trustworthy AI systems across diverse applications, from robotics and industrial simulations to safety-critical domains where reliable uncertainty quantification is paramount.