331 citations · 1.8k across the 71 of their papers we have counts for
4 papers · 2 filters
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…
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…
On the Geometry and Extremal Properties of the Edge-Degeneracy Model
Nicolas Kim, Dane Wilburne, Sonja Petrović +1
The edge-degeneracy model is an exponential random graph model that uses the graph degeneracy, a measure of the graph's connection density, and number of edges in a graph as its su…