Update 2024-icml-cp-any-distribution.md

_publications/2024-icml-cp-any-distribution.md

@@ -6,7 +6,7 @@ excerpt: 'Paper at ICML 2024. Demonstrates how conformal prediction can theoreti
 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.'
+citation: 'Prinster, D.*, Stanton, S.*, Saria, S., & Liu, A. (2024). Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them). 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)