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…
Nonparametric Inference For Density Modes
Christopher Genovese, Marco Perone-Pacifico, Isabella Verdinelli +1
We derive nonparametric confidence intervals for the eigenvalues of the Hessian at modes of a density estimate. This provides information about the strength and shape of modes and…
Nonparametric ridge estimation
Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli +1
We study the problem of estimating the ridges of a density function. Ridge estimation is an extension of mode finding and is useful for understanding the structure of a density. It…
Manifold estimation and singular deconvolution under Hausdorff loss
Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli +1
We find lower and upper bounds for the risk of estimating a manifold in Hausdorff distance under several models. We also show that there are close connections between manifold esti…
Minimax Manifold Estimation
Christopher Genovese, Marco Perone-Pacifico, Isabella Verdinelli +1
We find the minimax rate of convergence in Hausdorff distance for estimating a manifold M of dimension d embedded in R^D given a noisy sample from the manifold. We assume that the…
The Geometry of Nonparametric Filament Estimation
Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli +1
We consider the problem of estimating filamentary structure from planar point process data. We make some connections with computational geometry and we develop nonparametric method…
On the path density of a gradient field
Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli +1
We consider the problem of reliably finding filaments in point clouds. Realistic data sets often have numerous filaments of various sizes and shapes. Statistical techniques exist f…