Dimension-free PAC-Bayesian bounds for the estimation of the mean of a random vector
Statistics Theory
2018-02-14 v1 Statistics Theory
Abstract
In this paper, we present a new estimator of the mean of a random vector, computed by applying some threshold function to the norm. Non asymptotic dimension-free almost sub-Gaussian bounds are proved under weak moment assumptions, using PAC-Bayesian inequalities.
Keywords
Cite
@article{arxiv.1802.04308,
title = {Dimension-free PAC-Bayesian bounds for the estimation of the mean of a random vector},
author = {Olivier Catoni and Ilaria Giulini},
journal= {arXiv preprint arXiv:1802.04308},
year = {2018}
}
Comments
31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Selected for oral presentation in the NIPS 2017 workshop "(Almost) 50 Shades of Bayesian Learning: PAC-Bayesian trends and insights", December 9, 2017. Workshop URL : https://bguedj.github.io/nips2017/50shadesbayesian.html