Predictive Inference

Predictive inference aims to generate reliable uncertainty estimates around predictions made by machine learning models, going beyond simple point estimates. Current research focuses on developing methods that improve the accuracy and efficiency of these uncertainty estimates, particularly in scenarios with limited labeled data, utilizing techniques like prediction-powered inference (PPI), conformal prediction, and bootstrapping, often combined with various model architectures including neural networks and large language models. These advancements are crucial for enhancing the trustworthiness and reliability of machine learning applications across diverse fields, from healthcare and environmental science to social sciences and engineering, where understanding prediction uncertainty is paramount for informed decision-making.