Recent advances in single-cell RNA sequencing technologies enable deep insights into
cellular development, gene regulation, and phenotypic diversity by measuring gene …

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 …

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 …

Fields like public health, public policy, and social science often want to quantify the degree
of dependence between variables whose relationships take on unknown functional forms …

KSG mutual information estimator, which is based on the distances of each sample to its k-th
nearest neighbor, is widely used to estimate mutual information between two continuous …

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 …

This paper proposes a geometric estimator of dependency between a pair of multivariate
random variables. The proposed estimator of dependency is based on a randomly permuted …

We propose a scalable divergence estimation method based on hashing. Consider two
continuous random variables $ X $ and $ Y $ whose densities have bounded support. We …

Henze-Penrose divergence is a non-parametric divergence measure that can be used to
estimate a bound on the Bayes error in a binary classification problem. In this paper, we …