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+---
+title: "Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)"
+collection: publications
+permalink: /publication/2024-icml-cp-any-distribution
+excerpt: 'Paper at ICML 2024. 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.'
+date: 2024-07-21
+venue: 'The International Conference on Machine Learning (ICML)'
+paperurl: 'https://arxiv.org/abs/2405.06627'
+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.'
+---
+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](https://arxiv.org/abs/2405.06627)