71 citations · 184 across the 11 of their papers we have counts for
12 papers
Feature Importance: A Closer Look at Shapley Values and LOCO
Isabella Verdinelli, Larry Wasserman
There is much interest lately in explainability in statistics and machine learning. One aspect of explainability is to quantify the importance of various features (or covariates).…
Decorrelated Variable Importance
Isabella Verdinelli, Larry Wasserman
Because of the widespread use of black box prediction methods such as random forests and neural nets, there is renewed interest in developing methods for quantifying variable impor…
Forest Guided Smoothing
Isabella Verdinelli, Larry Wasserman
We use the output of a random forest to define a family of local smoothers with spatially adaptive bandwidth matrices. The smoother inherits the flexibility of the original forest…
Hybrid Wasserstein Distance and Fast Distribution Clustering
Isabella Verdinelli, Larry Wasserman
We define a modified Wasserstein distance for distribution clustering which inherits many of the properties of the Wasserstein distance but which can be estimated easily and comput…
Analysis of a Mode Clustering Diagram
Isabella Verdinelli, Larry Wasserman
Mode-based clustering methods define clusters to be the basins of attraction of the modes of a density estimate. The most common version is mean shift clus- tering which uses a gra…
Finding Singular Features
Christopher Genovese, Marco Perone-Pacifico, Isabella Verdinelli +1
We present a method for finding high density, low-dimensional structures in noisy point clouds. These structures are sets with zero Lebesgue measure with respect to the -dimensi…