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 …

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 …

An important goal common to domain adaptation and causal inference is to make accurate
predictions when the distributions for the source (or training) domain (s) and target (or test) …

In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …

Domain adaptation (DA) arises as an important problem in statistical machine learning when
the source data used to train a model is different from the target data used to test the model …

All famous machine learning algorithms that comprise both supervised and semi-supervised
learning work well only under a common assumption: the training and test data follow the …

This paper deals with the unsupervised domain adaptation problem, where one wants to
estimate a prediction function $ f $ in a given target domain without any labeled sample by …

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 …

Unsupervised domain adaptation, as a prevalent transfer learning setting, spans many real-
world applications. With the increasing representational power and applicability of neural …