Conformalized Neural Network Ensemble

Conformalized neural network ensembles aim to improve the reliability of neural network predictions by providing rigorous uncertainty quantification, ensuring predictions are accompanied by statistically guaranteed confidence intervals or sets. Current research focuses on adapting conformal prediction methods to various neural network architectures, including DeepONets and Graph Neural Networks, and exploring efficient algorithms for computing these confidence sets, particularly in high-dimensional spaces. This approach is significant because it addresses the critical need for trustworthy predictions in high-stakes applications like medical imaging and autonomous driving, where understanding and managing uncertainty is paramount.