Posterior conformal prediction

Oregon Convention Center 

Conformal prediction is a distribution-free method to quantify uncertainties in machine learning predictions. It can transform point predictions into marginally valid prediction intervals under the assumption of data exchangeability. However, many predictive tasks require conditional coverage, which is known to be unachievable unless we use intervals with infinite expected length. In this article, we propose a new data-adaptive weighting method to approximate the conditional coverage guarantee. Our method can improve the conditional coverage rate of conformal prediction without increasing the interval length excessively. Furthermore, we extend our method to other applications in predictive inference.

Conformal Prediction

Predictive Inference

Distribution-free inference 

Section on Statistical Learning and Data Science