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 …

Abstract Domain adaptation (DA) is an important and emerging field of machine learning
that tackles the problem occurring when the distributions of training (source domain) and test …

Domain adaptation is one of the most challenging tasks of modern data analytics. If the
adaptation is done correctly, models built on a specific data representation become more …

In many machine learning algorithms, a major assumption is that the training and the test
samples are in the same feature space and have the same distribution. However, for many …

We introduce a new representation learning approach for domain adaptation, in which data
at training and test time come from similar but different distributions. Our approach is directly …

We present a new and original method to solve the domain adaptation problem using
optimal transport. By searching for the best transportation plan between the probability …

Transferring knowledge from a source domain to a target domain by domain adaptation has
been an interesting and challenging problem in many machine learning applications. The …

Abstract The Domain Adaptation problem in machine learning occurs when the test and
training data generating distributions differ. We consider the covariate shift setting, where the …

In this paper, we tackle the problem of reducing discrepancies between multiple domains, ie
multi-source domain adaptation, and consider it under the target shift assumption: in all …

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 …