47 citations · 134 across the 37 of their papers we have counts for
5 papers · 1 filter
A Hierarchical Nearest Neighbour Approach to Contextual Bandits
Stephen Pasteris, Chris Hicks, Vasilios Mavroudis
In this paper we consider the adversarial contextual bandit problem in metric spaces. The paper "Nearest neighbour with bandit feedback" tackled this problem but when there are man…
Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning
Chris Hicks, Vasilios Mavroudis, Myles Foley +3
Communication networks able to withstand hostile environments are critically important for disaster relief operations. In this paper, we consider a challenging scenario where drone…
Reward Shaping for Happier Autonomous Cyber Security Agents
Elizabeth Bates, Vasilios Mavroudis, Chris Hicks
As machine learning models become more capable, they have exhibited increased potential in solving complex tasks. One of the most promising directions uses deep reinforcement learn…
Inroads into Autonomous Network Defence using Explained Reinforcement Learning
Myles Foley, Mia Wang, Zoe M +2
Computer network defence is a complicated task that has necessitated a high degree of human involvement. However, with recent advancements in machine learning, fully autonomous net…
Nearest Neighbour with Bandit Feedback
Stephen Pasteris, Chris Hicks, Vasilios Mavroudis
In this paper we adapt the nearest neighbour rule to the contextual bandit problem. Our algorithm handles the fully adversarial setting in which no assumptions at all are made abou…