Su‐In Lee

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 …

Explaining a Series of Models by Propagating Shapley Values

2021 · arXiv (Cornell University)

Local feature attribution methods are increasingly used to explain complex machine learning models. However, current methods are limited because they are extremely expensive to compute or are not capable of explaining a distributed series of …

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 …