Mobile crowd sensing (MCS) is the special case of crowdsourcing, which leverages the
smartphones with various embedded sensors and user's mobility to sense diverse …

This paper provides an overview of the research conducted in the context of structural (or
structured) systems. These are parametrized models used to assess and design system …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

Coresets are small data summaries that are sufficient for model training. They can be
maintained online, enabling efficient handling of large data streams under resource …

Many machine learning tasks involve solving complex optimization problems, such as
working on non-differentiable, non-continuous, and non-unique objective functions; in some …

It is generally believed that submodular functions–and the more general class of $\gamma $-
weakly submodular functions–may only be optimized under the non-negativity assumption …

Individual's opinions are fundamentally shaped and evolved by their interactions with other
people, and social phenomena such as disagreement and polarization are now tightly …

We connect high-dimensional subset selection and submodular maximization. Our results
extend the work of Das and Kempe [In ICML (2011) 1057–1064] from the setting of linear …

In reinforcement learning (RL), rewards of states are typically considered additive, and
following the Markov assumption, they are $\textit {independent} $ of states visited …

Most supervised learning models are trained for full automation. However, their predictions
are sometimes worse than those by human experts on some specific instances. Motivated by …