Multitask learning (MTL) is a machine learning technique where a problem is learnt together
with other related problems at the same time, using a shared representation, so that …

Multitask learning (MTL) via a shared representation has been adopted to alleviate
problems with sparsity of labeled data across different learning tasks. Active learning, on the …

Multi-task learning (MTL) methods have shown promising performance by learning multiple
relevant tasks simultaneously, which exploits to share useful information across relevant …

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 …

This paper introduces two new frameworks, Doubly Supervised Latent Dirichlet Allocation
(DSLDA) and its non-parametric variation (NP-DSLDA), that integrate two different types of …

The cost of annotating training data has traditionally been a bottleneck for supervised
learning approaches. The problem is further exacerbated when supervised learning is …

In multi-task learning (MTL), multiple related tasks are learned jointly by sharing information
across them. Many MTL algorithms have been proposed to learn the underlying task groups …

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 …

The Multi-Task Learning (MTL) technique has been widely studied by word-wide
researchers. The majority of current MTL studies adopt the hard parameter sharing structure …

In this paper, we propose an online multitask learning framework where the weight vectors
are updated in an adaptive fashion based on inter-task relatedness. Our work is in contrast …