Stacking regressions is an ensemble technique that forms linear combinat...
In previous literature, backward error analysis was used to find ordinar...
We study the fundamental limits of matching pursuit, or the pure greedy
...
Decision tree learning is increasingly being used for pointwise inferenc...
We develop a theoretical framework for the analysis of oblique decision
...
This paper shows that decision trees constructed with Classification and...
Decision trees and their ensembles are endowed with a rich set of diagno...
Within the machine learning community, the widely-used uniform convergen...
Decision trees with binary splits are popularly constructed using
Classi...
Classical results on the statistical complexity of linear models have
co...
Decision trees with binary splits are popularly constructed using
Classi...
For any ReLU network there is a representation in which the sum of the
a...
It has been experimentally observed in recent years that multi-layer
art...
Random forests have become an important tool for improving accuracy in
r...
A popular class of problem in statistics deals with estimating the suppo...
Applied researchers often construct a network from a random sample of no...
Learning properties of large graphs from samples has been an important
p...
The MDL two-part coding index of resolvability provides a
finite-sampl...
We give convergence guarantees for estimating the coefficients of a symm...
Estimation of functions of d variables is considered using ridge
combi...
Recently, a general method for analyzing the statistical accuracy of the...
We establish sup-norm error bounds for functions that are approximated b...
Let f^ be a function on R^d satisfying a spectral
norm condition. Fo...