Estimating mutual information from independent identically distributed samples drawn from
an unknown joint density function is a basic statistical problem of broad interest with …

The Mutual Information (MI) is an often used measure of dependency between two random
variables utilized in information theory, statistics and machine learning. Recently several MI …

This paper studies convergence of empirical measures smoothed by a Gaussian kernel.
Specifically, consider approximating P* N σ, for N σ=△ N (0, σ 2 I d), by P̑ n* N σ under …

We derive the mean squared error convergence rates of kernel density-based plug-in
estimators of mutual information measures between two multidimensional random variables …

We propose a direct estimation method for Rényi and f-divergence measures based on a
new graph theoretical interpretation. Suppose that we are given two sample sets X and Y …

We analyze bias correction methods using jackknife, bootstrap, and Taylor series. We focus
on the binomial model, and consider the problem of bias correction for estimating f (p) …

In the context of supervised learning, meta learning uses features, metadata and other
information to learn about the difficulty, behavior, or composition of the problem. Using this …

Recent work has focused on the problem of nonparametric estimation of information
divergence functionals between two continuous random variables. Many existing …

We study the problem of using iid samples from an unknown multivariate probability
distribution p to estimate the mutual information of p. This problem has recently received …

We address the problem of learning to benchmark the best achievable classifier
performance. In this problem the objective is to establish statistically consistent estimates of …