151 citations · 692 across the 40 of their papers we have counts for
15 papers · 1 filter
A Generic Approach for Reproducible Model Distillation
Yunzhe Zhou, Peiru Xu, Giles Hooker
Model distillation has been a popular method for producing interpretable machine learning. It uses an interpretable "student" model to mimic the predictions made by the black box "…
The Infinitesimal Jackknife and Combinations of Models
Indrayudh Ghosal, Yunzhe Zhou, Giles Hooker
The Infinitesimal Jackknife is a general method for estimating variances of parametric models, and more recently also for some ensemble methods. In this paper we extend the Infinit…
S-LIME: Stabilized-LIME for Model Explanation
Zhengze Zhou, Giles Hooker, Fei Wang
An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are…
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
Benjamin Lengerich, Sarah Tan, Chun-Hao Chang +2
Models which estimate main effects of individual variables alongside interaction effects have an identifiability challenge: effects can be freely moved between main effects and int…
Unbiased Measurement of Feature Importance in Tree-Based Methods
Zhengze Zhou, Giles Hooker
We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods. These methods have been shown to be biase…
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…