JAWS: Auditing Predictive Uncertainty Under Covariate Shift ([previously] Predictive Inference Under Covariate Shift)
Published in Advances in Neural Information Processing Systems (NeurIPS), 2022
Recommended citation: Prinster, D., Liu, A., & Saria, S. (2022). JAWS: Auditing Predictive Uncertainty Under Covariate Shift. In Advances in Neural Information Processing Systems. https://papers.nips.cc/paper_files/paper/2022/hash/e944bacecce6b06374ac39b260348db0-Abstract-Conference.html
JAWS is a collection of wrapper methods for distribution-free predictive inference when the common data exchangeability (e.g., i.i.d.) assumption is violated due to shifts in the input data distribution (standard covariate shifts). JAWS is based on our core method JAW–the JAckknife+ Weighted for standard covariate shift–and also includes computationally efficient Approximations of JAW (JAWA) using higher-order influence functions. PDF