The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …

As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Generative adversarial networks (GANs) provide an algorithmic framework for constructing
generative models with several appealing properties: they do not require a likelihood …

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 …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

We address the problem of visual classification with multiple features and/or multiple
instances. Motivated by the recent success of multitask joint covariate selection, we …

In many real-world applications we deal with multiple related classification/regression/
clustering tasks. For example, in the prediction of therapy outcome (Bickel et al., 2008), the …

Previous works on image emotion analysis mainly focused on predicting the dominant
emotion category or the average dimension values of an image for affective image …

Multi-task learning (MTL) learns multiple related tasks simultaneously to improve
generalization performance. Alternating structure optimization (ASO) is a popular MTL …