Date of Original Version
Copyright 2014 by the author(s)
Abstract or Description
Estimating divergences between probability distributions in a consistent way is of great importance in many machine learning tasks. Although this is a fundamental problem in nonparametric statistics, to the best of our knowledge there has been no finite sample exponential inequality convergence bound derived for any divergence estimators. The main contribution of our work is to provide such a bound for an estimator of Renyi divergence for a smooth Holder class of densities on the d-dimensional unit cube. We also illustrate our theoretical results with a numerical experiment.
Journal of Machine Learning Research : Workshop and Conference Proceedings, 32, 333-341.