Multi-task learning is the problem of maximizing the performance of a system across a number of related tasks. When applied to multiple domains for the same task, it is similar to domain adaptation, but symmetric, rather than limited to improving performance on a target domain. We present a more principled, better performing model for this problem, based on the use of a hierarchical Bayesian pr...

This paper presents a series of new results for domain adaptation in the regression setting. We prove that the discrepancy is a distance for the squared loss when the hypothesis set is the reproducing kernel Hilbert space induced by a universal kernel such as the Gaussian kernel. We give new pointwise loss guarantees based on the discrepancy of the empirical source and target distributions for ...

Supervised learning with large scale labeled datasets and deep layered models has made a paradigm shift in diverse areas in learning and recognition. However, this approach still suffers generalization issues under the presence of a domain shift between the training and the test data distribution. In this regard, unsupervised domain adaptation algorithms have been proposed to directly address t...

Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. Unsupervised domain adaptation aims to address this challenge, though current approaches do not utilise the continuity of the occurring shifts. Many robotic applications exhibit these conditions and thus facilitate the potential to incrementally adapt a...

Domain adaptation aims to learn a transferable model bridge the domain shift between one labeled source and another sparsely or unlabeled target domain. Since data may be collected from multiple sources, multi-source (MDA) has attracted increasing attention. Recent MDA methods do not consider pixel-level alignment sources misalignment across different sources. In this paper, we propose novel fr...

Automatic authorship attribution, by its nature, is much more advantageous if it is domain (i.e., topic and/or genre) independent. That is, many real world problems that require authorship attribution may not have in-domain training data readily available. However, most previous work based on machine learning techniques focused only on in-domain text for authorship attribution. In this paper, w...

In this paper, a system for the extraction of key argument phrases – which make the opinion holder feel negative or positive towards a particular product – from product reviews is introduced. Since the necessary amount of training examples from any arbitrary product type (target domain) is not always available, the possible usage of domain adaptation in the task of opinion phrase extraction is ...

Lexical and acoustic markers in spoken language can be used to detect mild cognitive impairment (MCI), a condition which is often a precursor to dementia and frequently causes some degree of dysphasia. Research to develop such a diagnostic tool for clinicians has been hindered by the scarcity of available data. This work uses domain adaptation to adapt Alzheimer’s data to improve classification...