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 …

Domain adaptation studies learning algorithms that generalize across source domains and
target domains that exhibit different distributions. Recent studies reveal that deep neural …

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 class of kernel-based two-sample tests, which aim to determine whether two
sets of samples are drawn from the same distribution. Our tests are constructed from kernels …

Learning methods are challenged when there is not enough labelled data. It gets worse
when the existing learning data have different distributions in different domains. To deal with …

We propose a series of computationally efficient, nonparametric tests for the two-sample,
independence and goodness-of-fit problems, using the Maximum Mean Discrepancy …

We propose two novel nonparametric two-sample kernel tests based on the Maximum Mean
Discrepancy (MMD). First, for a fixed kernel, we construct an MMD test using either …

We propose novel statistics which maximise the power of a two-sample test based on the
Maximum Mean Discrepancy (MMD), byadapting over the set of kernels used in defining it …

In this paper, we study challenging anomaly detections in streaming videos under fully
unsupervised settings. Unsupervised unmasking methods [12] have recently been applied …