5 papers
PolySHAP: Extending KernelSHAP with Interaction-Informed Polynomial Regression
Fabian Fumagalli, R. Teal Witter, Christopher Musco
Shapley values have emerged as a central game-theoretic tool in explainable AI (XAI). However, computing Shapley values exactly requires game evaluations for a model with …
Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values
R. Teal Witter, Yurong Liu, Christopher Musco
With origins in game theory, probabilistic values like Shapley values, Banzhaf values, and semi-values have emerged as a central tool in explainable AI. They are used for feature a…
Efficiently Constructing Sparse Navigable Graphs
Alex Conway, Laxman Dhulipala, Martin Farach-Colton +6
Graph-based nearest neighbor search methods have seen a surge of popularity in recent years, offering state-of-the-art performance across a wide variety of applications. Central to…
Provably Accurate Shapley Value Estimation via Leverage Score Sampling
Christopher Musco, R. Teal Witter
Originally introduced in game theory, Shapley values have emerged as a central tool in explainable machine learning, where they are used to attribute model predictions to specific…
Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf Values
Yurong Liu, R. Teal Witter, Flip Korn +4
Banzhaf values provide a popular, interpretable alternative to the widely-used Shapley values for quantifying the importance of features in machine learning models. Like Shapley va…