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 …

Estimating conditional mutual information (CMI) is an essential yet challenging step in many
machine learning and data mining tasks. Estimating CMI from data that contains both …

Mutual information is a general statistical dependency measure which has found
applications in representation learning, causality, domain generalization and computational …

We consider the problem of non-parametric Conditional Independence testing (CI testing)
for continuous random variables. Given iid samples from the joint distribution $ f (x, y, z) $ of …

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 …

Since its inception, the neural estimation of mutual information (MI) has demonstrated the
empirical success of modeling expected dependency between high-dimensional random …

We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of
the association between one or several features and a given outcome, conditional on a …

Conditional independence testing is a fundamental problem underlying causal discovery
and a particularly challenging task in the presence of nonlinear dependencies. Here a fully …

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 …

Conditional independence (CI) testing is a fundamental and challenging task in modern
statistics and machine learning. Many modern methods for CI testing rely on powerful …