Search Results for author:
Found 2 papers, 0 papers with code
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations
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
Minimax Bounds for Generalized Linear Models
We establish a new class of minimax prediction error bounds for generalized linear models.
