Recently, mutual information (MI) has become widely recognized as a statistical measure of
dependence that is suitable for applications where data are non-Gaussian, or where the …

Quantifying the dependence between two random variables is a fundamental issue in data
analysis, and thus many measures have been proposed. Recent studies have focused on …

Mutual information is a popular metric in machine learning. In case of a discrete target
variable and a continuous feature variable the mutual information can be calculated as a …

The estimation of mutual information (MI) or conditional mutual information (CMI) from a set
of samples is a longstanding problem. A recent line of work in this area has leveraged the …

Squared-loss mutual information (SMI) is a robust measure of the statistical dependence
between random variables. The sample-based SMI approximator called least-squares …

Estimating mutual information (MI) from samples is a fundamental problem in statistics,
machine learning, and data analysis. Recently it was shown that a popular class of non …

Mutual Information (MI) is a powerful concept from information theory used in many
application fields. For practical tasks it is often necessary to estimate the Mutual Information …

Abstract Information Noise Contrastive Estimation (InfoNCE) is a popular neural estimator of
mutual information (MI). While InfoNCE has demonstrated impressive results in …

We present a new method for estimating mutual information based on the random forests
classifiers. This method uses random permutation of one of the two variables to create data …

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