17 citations · 27 across the 6 of their papers we have counts for
13 papers
Nonparametric Variable Screening with Optimal Decision Stumps
Jason M. Klusowski, Peter M. Tian
Decision trees and their ensembles are endowed with a rich set of diagnostic tools for ranking and screening variables in a predictive model. Despite the widespread use of tree bas…
Good Classifiers are Abundant in the Interpolating Regime
Ryan Theisen, Jason M. Klusowski, Michael W. Mahoney
Within the machine learning community, the widely-used uniform convergence framework has been used to answer the question of how complex, over-parameterized models can generalize w…
Sparse learning with CART
Jason M. Klusowski
Decision trees with binary splits are popularly constructed using Classification and Regression Trees (CART) methodology. For regression models, this approach recursively divides t…
Global Capacity Measures for Deep ReLU Networks via Path Sampling
Ryan Theisen, Jason M. Klusowski, Huan Wang +3
Classical results on the statistical complexity of linear models have commonly identified the norm of the weights as a fundamental capacity measure. Generalizations of this…
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
Zhiqi Bu, Jason Klusowski, Cynthia Rush +1
SLOPE is a relatively new convex optimization procedure for high-dimensional linear regression via the sorted l1 penalty: the larger the rank of the fitted coefficient, the larger…
Analyzing CART
Jason M. Klusowski
Decision trees with binary splits are popularly constructed using Classification and Regression Trees (CART) methodology. For binary classification and regression models, this appr…