no code implementations • 24 Feb 2022 • Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar
We formalize the desiderata of value functions that respect both the model and the data manifold in a set of axioms and are robust to perturbation on off-manifold regions, and show that there exists a unique value function that satisfies these axioms, which we term the Joint Baseline value function, and the resulting Shapley value the Joint Baseline Shapley (JBshap), and validate the effectiveness of JBshap in experiments.
Explainable Artificial Intelligence (XAI)
Feature Importance
no code implementations • NeurIPS 2020 • Kuan-Yun Lee, Thomas Courtade
We establish a new class of minimax prediction error bounds for generalized linear models.