Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)
Published in The International Conference on Machine Learning (ICML), 2024
Recommended citation: Prinster, D.*, Stanton, S.*, Saria, S., & Liu, A. (2023). JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift. In International Conference on Machine Learning. PMLR. https://arxiv.org/abs/2405.06627
Demonstrates how conformal prediction can theoretically extend to any data distribution (i.e., not only exchangeable or quasi-exchangeable ones), with practical experiments focused on common settings of AI/ML agents including multiround synthetic protein design and active learning. PDF