7.6k citations · 9.7k across the 31 of their papers we have counts for
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Learning Deep Attribution Priors Based On Prior Knowledge
Ethan Weinberger, Joseph Janizek, Su-In Lee
Feature attribution methods, which explain an individual prediction made by a model as a sum of attributions for each input feature, are an essential tool for understanding the beh…
Explaining Models by Propagating Shapley Values of Local Components
Hugh Chen, Scott Lundberg, Su-In Lee
In healthcare, making the best possible predictions with complex models (e.g., neural networks, ensembles/stacks of different models) can impact patient welfare. In order to make t…
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Gabriel Erion, Joseph D. Janizek, Pascal Sturmfels +2
Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whos…
Explainable AI for Trees: From Local Explanations to Global Understanding
Scott M. Lundberg, Gabriel Erion, Hugh Chen +7
Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are the most popular non-linear predictive models used in practice today, yet…