151 citations · 692 across the 40 of their papers we have counts for
Showing 2018 · stat.MLShow all
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stat.ML2018★ 13 cited
Approximation Trees: Statistical Stability in Model Distillation
Yichen Zhou, Zhengze Zhou, Giles Hooker
This paper examines the stability of learned explanations for black-box predictions via model distillation with decision trees. One approach to intelligibility in machine learning…
stat.ML2018★ 50 cited
Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and its Variance Estimate
Indrayudh Ghosal, Giles Hooker
In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit we extract…
stat.ML2018★ 33 cited
Considerations When Learning Additive Explanations for Black-Box Models
Sarah Tan, Giles Hooker, Paul Koch +2
Many methods to explain black-box models, whether local or global, are additive. In this paper, we study global additive explanations for non-additive models, focusing on four expl…