Asynchronous and parallel implementation of standard reinforcement learning (RL)
algorithms is a key enabler of the tremendous success of modern RL. Among many …

Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with
others or devices. In this paper, multi-task learning with denoising and classification tasks is …

Multi-task learning aims to acquire a set of functions, either regressors or classifiers, that
perform well for diverse tasks. At its core, the idea behind multi-task learning is to exploit the …

Motivated by applications, we consider here new operator theoretic approaches to
Conditional mean embeddings (CME). Our present results combine a spectral analysis …

Apprenticeship learning is a framework in which an agent learns a policy to perform a given
task in an environment using example trajectories provided by an expert. In the real world …

In this paper we consider a problem known as multi-task learning, consisting of fitting a set of
classifier or regression functions intended for solving different tasks. In our novel formulation …

Graphs are powerful mathematical tools that enable modeling of complex systems. Graph
machine learning exploits possibly unknown data structures, and provides a unified …

Next-generation wireless networks must support a plethora of new applications ranging from
the Internet of Things to virtual reality. Each one of these emerging applications have unique …