This repository contains code for both a 2023 ICML paper a 2023 COPA extended abstract:
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Drew Prinster, Suchi Saria, and Anqi Liu. JAWS-X: Addressing efficiency bottlenecks of conformal prediction under standard and feedback covariate shift. International Conference on Machine Learning (ICML), 2023.
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Drew Prinster, Suchi Saria, and Anqi Liu. Efficient Approximate Predictive Inference Under Feedback Covariate Shift with Influence Functions. Conformal and Probabilistic Prediction with Applications (COPA), 2023.*
We also build heavily on code from the following paper: Clara Fannjiang, Stephen Bates, Anastasios N Angelopoulos, Jennifer Listgarten, and Michael I Jordan. Conformal prediction under feedback covariate shift for biomolecular design. Proceedings of the National Academy of Sciences, 119(43):e2204569119, 2022.
This repository was last updated and cleaned up on August 11th, 2023! The code should now be ready to clone and play with, though I will continue to work improving the repo's useability and clarity throughout September 2023. Please don't hestitate to reach out at drew@cs.jhu.edu with questions!
*Code and experimental details for "Efficient Predictive Interval Approximation Under Feedback Covariate Shift with Higher-Order Influence Functions" (COPA 2023):
For the main experiment presented in Figure 1 of the extended abstract, a neural network predictor