Learning subgaussian classes : Upper and minimax bounds
Statistics Theory
2016-09-20 v2 Statistics Theory
Abstract
We obtain sharp oracle inequalities for the empirical risk minimization procedure in the regression model under the assumption that the target Y and the model F are subgaussian. The bound we obtain is sharp in the minimax sense if F is convex. Moreover, under mild assumptions on F, the error rate of ERM remains optimal even if the procedure is allowed to perform with constant probability. A part of our analysis is a new proof of minimax results for the gaussian regression model.
Cite
@article{arxiv.1305.4825,
title = {Learning subgaussian classes : Upper and minimax bounds},
author = {Guillaume Lecué and Shahar Mendelson},
journal= {arXiv preprint arXiv:1305.4825},
year = {2016}
}
Comments
learning theory, empirical process, minimax rates, Topics in Learning Theory - Societe Mathematique de France, (S. Boucheron and N. Vayatis Eds.). 2016