Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains. Traditional domain adaptation only considers adapting for one task. In this paper, we explore multi-task representation learning under the domain adaptation scenario. We propose a neural network framework that supports domain adaptation for mul...

Domain adaptation is an important technology to handle domain dependence problem in sentiment analysis field. Existing methods usually rely on sentiment classifiers trained in source domains. However, their performance may heavily decline if the distributions of sentiment features in source and target domains have significant difference. In this paper, we propose an active sentiment domain adap...

In this paper, we harness the synergy between two important learning paradigms, namely, active learning and domain adaptation. We show how active learning in a target domain can leverage information from a different but related source domain. Our proposed framework, Active Learning Domain Adapted (Alda), uses source domain knowledge to transfer information that facilitates active learning in th...

Knowledge transfer across several streaming processes remain challenging problem not only because of different distributions each stream but also rapidly changing and never-ending environments data streams. Albeit growing research achievements in this area, most existing works are developed for a single source domain which limits its resilience to exploit multi-source domains being beneficial r...

Adaptation of a classifier to new domains is one the challenging problems in machine learning. This has been addressed using many deep and non-deep learning based methods. Among methodologies used, that adversarial widely applied solve along with domain adaptation. These methods are on discriminator ensures source target distributions close. However, here we suggest rather than point estimate o...

Domain Adaptation (DA) aims to generalize the classifier learned from a well-labeled source domain an unlabeled target domain. Existing DA methods usually assume that rich labels could be available in However, we confront with large number of data but only few labeled data, and thus, how transfer knowledge this sparsely-labeled is still challenge, which greatly limits its application wild. This...

The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting of transfer learning or domain adaptation: Here, training data from a source domain, aim to learn a classifier which performs well on a target domain governed by a different distribution. We pursue an agnostic approa...