Empirical Bounds for Functions with Weak Interactions
Abstract
We provide sharp empirical estimates of expectation, variance and normal approximation for a class of statistics whose variation in any argument does not change too much when another argument is modified. Examples of such weak interactions are furnished by U- and V-statistics, Lipschitz L-statistics and various error functionals of L2-regularized algorithms and Gibbs algorithms.
Cite
Text
Maurer and Pontil. "Empirical Bounds for Functions with Weak Interactions." Annual Conference on Computational Learning Theory, 2018.Markdown
[Maurer and Pontil. "Empirical Bounds for Functions with Weak Interactions." Annual Conference on Computational Learning Theory, 2018.](https://mlanthology.org/colt/2018/maurer2018colt-empirical/)BibTeX
@inproceedings{maurer2018colt-empirical,
title = {{Empirical Bounds for Functions with Weak Interactions}},
author = {Maurer, Andreas and Pontil, Massimiliano},
booktitle = {Annual Conference on Computational Learning Theory},
year = {2018},
pages = {987-1010},
url = {https://mlanthology.org/colt/2018/maurer2018colt-empirical/}
}