In many practical applications, such as poker, chess, drug interdiction, cybersecurity, and
national defense, players often have adversarial stances, ie, the selfish actions of each …

We analyze a scenario in which software agents implemented as regret-minimizing
algorithms engage in a repeated auction on behalf of their users. We study first-price and …

The usage of automated learning agents is becoming increasingly prevalent in many online
economic applications such as online auctions and automated trading. Motivated by such …

We present a study on a repeated delegated choice problem, which is the first to consider an
online learning variant of Kleinberg and Kleinberg, EC'18. In this model, a principal interacts …

We consider the setting of repeated fair division between two players, denoted Alice and
Bob, with private valuations over a cake. In each round, a new cake arrives, which is …

Consider N players that each uses a mixture of K resources. Each of the players' reward
functions includes a linear pricing term for each resource that is controlled by the game …

Existing work on trustworthy machine learning (ML) often concentrates on individual aspects
of trust, such as fairness or privacy. Additionally, many techniques overlook the distinction …

A ubiquitous learning problem in today's digital market is, during repeated interactions
between a seller and a buyer, how a seller can gradually learn optimal pricing decisions …

When learning in strategic environments, a key question is whether agents can overcome
uncertainty about their preferences to achieve outcomes they could have achieved absent …

In this article we survey the main research topics of our group at the University of Essex. Our
research interests lie at the intersection of theoretical computer science, artificial …