JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift
Published in The Fortieth International Conference on Machine Learning (ICML), 2023
Accepted for an Oral Presentation at ICML 2023 (top ~2% of submissions) and building on our previous “JAWS” framework (NeurIPS 2022), this paper presents JAWS-X, a collection of methods for efficiently estimating predictive confidence intervals for black-box predictors under standard and feedback covariate shift. Our JAWS-X methods achieve distribution-free, finite-sample guarantees while flexibly balancing statistical and computational efficiency.
Recommended citation: Prinster, D., 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://proceedings.mlr.press/v202/prinster23a.html