Abstract Domain adaptation arises in supervised learning when the training (source domain)
and test (target domain) data have different distributions. Let X and Y denote the features …

This paper is concerned with the problem of domain adaptation with multiple sources from a
causal point of view. In particular, we use causal models to represent the relationship …

Let X denote the feature and Y the target. We consider domain adaptation under three
possible scenarios:(1) the marginal P_Y changes, while the conditional P_X| Y stays the …

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 …

Abstract Domain adaptation generalizes a learning model across source domain and target
domain that follow different distributions. Most existing work follows a two-step procedure …

Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

Domain adaptation is the supervised learning setting in which the training and test data are
sampled from different distributions: training data is sampled from a source domain, whilst …

Learning domain-invariant representations has become a popular approach to
unsupervised domain adaptation and is often justified by invoking a particular suite of …

This paper is concerned with data-driven unsupervised domain adaptation, where it is
unknown in advance how the joint distribution changes across domains, ie, what factors or …

Unsupervised domain adaptation is critical to many real-world applications where label
information is unavailable in the target domain. In general, without further assumptions, the …