490 citations · 3.5k across the 123 of their papers we have counts for
9 papers · 1 filter
Bootstrapping and Sample Splitting For High-Dimensional, Assumption-Free Inference
Alessandro Rinaldo, Larry Wasserman, Max G'Sell +1
Several new methods have been proposed for performing valid inference after model selection. An older method is sampling splitting: use part of the data for model selection and par…
Topological Data Analysis
Larry Wasserman
Topological Data Analysis (TDA) can broadly be described as a collection of data analysis methods that find structure in data. This includes: clustering, manifold estimation, nonli…
Least Ambiguous Set-Valued Classifiers with Bounded Error Levels
Mauricio Sadinle, Jing Lei, Larry Wasserman
In most classification tasks there are observations that are ambiguous and therefore difficult to correctly label. Set-valued classifiers output sets of plausible labels rather tha…
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…
Statistical Inference for Cluster Trees
Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan +2
A cluster tree provides a highly-interpretable summary of a density function by representing the hierarchy of its high-density clusters. It is estimated using the empirical tree, w…
Minimax Rates for Estimating the Dimension of a Manifold
Jisu Kim, Alessandro Rinaldo, Larry Wasserman
Many algorithms in machine learning and computational geometry require, as input, the intrinsic dimension of the manifold that supports the probability distribution of the data. Th…