1 code implementation • 11 Jun 2023 • Yue Gao, Garvesh Raskutti, Rebecca Willet
This paper introduces a novel, computationally-efficient algorithm for predictive inference (PI) that requires no distributional assumptions on the data and can be computed faster than existing bootstrap-type methods for neural networks.
1 code implementation • 19 Jul 2022 • Yue Gao, Abby Stevens, Rebecca Willet, Garvesh Raskutti
Recently, there has been a proliferation of model-agnostic methods to measure variable importance (VI) that analyze the difference in predictive power between a full model trained on all variables and a reduced model that excludes the variable(s) of interest.
