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 …

Abstract Machine learning (ML) has been rapidly transforming the landscape of natural
sciences and has the potential to revolutionize the process of data analysis and hypothesis …

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 …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …

Online learning is a well established learning paradigm which has both theoretical and
practical appeals. The goal of online learning is to make a sequence of accurate predictions …

In recent years, reinforcement learning systems with general goals beyond a cumulative
sum of rewards have gained traction, such as in constrained problems, exploration, and …

We discuss a general method to learn data representations from multiple tasks. We provide
a justification for this method in both settings of multitask learning and learning-to-learn. The …

The multi-label classification problem has generated significant interest in recent years.
However, existing approaches do not adequately address two key challenges:(a) scaling up …

The contraction inequality for Rademacher averages is extended to Lipschitz functions with
vector-valued domains, and it is also shown that in the bounding expression the …

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