Ian Covert

Understanding Global Feature Contributions With Additive Importance Measures

2020 · arXiv (Cornell University)

Understanding the inner workings of complex machine learning models is a long-standing problem and most recent research has focused on local interpretability. To assess the role of individual input features in a global sense, we …

On the Robustness of Removal-Based Feature Attributions

2023 · arXiv (Cornell University)

To explain predictions made by complex machine learning models, many feature attribution methods have been developed that assign importance scores to input features. Some recent work challenges the robustness of these methods by showing that …