We investigate emerging mobile crowd sensing (MCS) systems, in which new cloud-based
platforms sequentially allocate homogenous sensing jobs to dynamically-arriving users with …

One of the fundamental issues in crowdsourcing is the trade-off between the number of
workers needed for high-accuracy aggregation and the budget to pay. To save cost, it is …

The multi-armed bandit (MAB) problem is a widely studied problem in machine learning
literature in the context of online learning. In this article, our focus is on a specific class of …

We study a problem of allocating divisible jobs, arriving online, to workers in a
crowdsourcing setting which involves learning two parameters of strategically behaving …

With algorithms, artificial intelligence, and machine learning becoming ubiquitous in our
society, we need to start thinking about the implications and ethical concerns of new …

In this work, we explore an online reinforcement learning problem called the multi-armed
bandit for application to improving outcomes in a web marketing context. Specifically, we …