Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …

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 …

We present a variety of new architectural features and training procedures that we apply to
the generative adversarial networks (GANs) framework. Using our new techniques, we …

Deep networks have been successfully applied to learn transferable features for adapting
models from a source domain to a different target domain. In this paper, we present joint …

This book is a foundational guide to graph representation learning, including state-of-the art
advances, and introduces the highly successful graph neural network (GNN) formalism …

In this paper, we tackle the problem of domain generalization: how to learn a generalized
feature representation for an “unseen” target domain by taking the advantage of multiple …

We propose a framework for analyzing and comparing distributions, which we use to
construct statistical tests to determine if two samples are drawn from different distributions …

Domain adaptation allows knowledge from a source domain to be transferred to a different
but related target domain. Intuitively, discovering a good feature representation across …

A comprehensive introduction to Support Vector Machines and related kernel methods. In
the 1990s, a new type of learning algorithm was developed, based on results from statistical …

We propose two statistical tests to determine if two samples are from different distributions.
Our test statistic is in both cases the distance between the means of the two samples …