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 …

Advances in biological and medical technologies have been providing us explosive
volumes of biological and physiological data, such as medical images …

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 …

The lasso and elastic net are popular regularized regression models for supervised
learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient …

Multi-task learning in Convolutional Networks has displayed remarkable success in the field
of recognition. This success can be largely attributed to learning shared representations …

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 …

Low-rank matrices play a fundamental role in modeling and computational methods for
signal processing and machine learning. In many applications where low-rank matrices …

In this paper, we focus on the problem of completion of multidimensional arrays (also
referred to as tensors), in particular three-dimensional (3-D) arrays, from limited sampling …

Abstract Multi-task learning (MTL), which optimizes multiple related learning tasks at the
same time, has been widely used in various applications, including natural language …

We investigate multi-task learning approaches that use a shared feature representation for
all tasks. To better understand the transfer of task information, we study an architecture with …