151 citations · 692 across the 40 of their papers we have counts for
5 papers · 1 filter
Asymptotic Properties for Methods Combining Minimum Hellinger Distance Estimates and Bayesian Nonparametric Density Estimates
Yuefeng Wu, Giles Hooker
In frequentist inference, minimizing the Hellinger distance between a kernel density estimate and a parametric family produces estimators that are both robust to outliers and stati…
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…
Local Quadratic Estimation of the Curvature in a Functional Single Index Model
Zi Ye, Giles Hooker
The nonlinear effects of environmental variability on species abundance plays an important role in the maintenance of ecological diversity. Nonetheless, many common models use para…
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…
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…