2026
Aggregate Models, Not Explanations: Improving Feature Importance Estimation
ICML 2026poster
Feature-importance methods show promise for transforming machine learning (ML) models from predictive engines into tools for scientific discovery. However, expressive models can be unstable due to data sampling and algorithmic stochasticity, leading to inaccurate variable importance estimates, under…