A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

Example-based explanations are widely used in the effort to improve the interpretability of
highly complex distributions. However, prototypes alone are rarely sufficient to represent the …

Computing optimal transport distances such as the earth mover's distance is a fundamental
problem in machine learning, statistics, and computer vision. Despite the recent introduction …

The goal of two-sample tests is to assess whether two samples, $ S_P\sim P^ n $ and $
S_Q\sim Q^ m $, are drawn from the same distribution. Perhaps intriguingly, one relatively …

We propose a nonparametric statistical test for goodness-of-fit: given a set of samples, the
test determines how likely it is that these were generated from a target density function. The …

Tree ensembles, such as random forests, are renowned for their high prediction
performance. However, their interpretability is critically limited due to the enormous …

We propose a method to optimize the representation and distinguishability of samples from
two probability distributions, by maximizing the estimated power of a statistical test based on …

There is currently a great expansion of the impact of machine learning algorithms on our
lives, prompting the need for objectives other than pure performance, including fairness …

We propose a class of nonparametric two-sample tests with a cost linear in the sample size.
Two tests are given, both based on an ensemble of distances between analytic functions …

Two semimetrics on probability distributions are proposed, given as the sum of differences of
expectations of analytic functions evaluated at spatial or frequency locations (ie, features) …