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 useful for detecting statistical independence between random
variables, and it has been successfully applied to solving various machine learning …

Learning representations in an unsupervised or self-supervised manner is a growing area of
research. Current approaches in representation learning seek to maximize the mutual …

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 …

Abstract Conditional Mutual Information (CMI) is a measure of conditional dependence
between random variables X and Y, given another random variable Z. It can be used to …

Mutual information (MI) is a fundamental quantity in information theory and machine
learning. However, direct estimation of MI is intractable, even if the true joint probability …

Mutual information (MI) is a fundamental measure of statistical dependence, with a myriad of
applications to information theory, statistics, and machine learning. While it possesses many …

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 …