41 citations · 82 across the 22 of their papers we have counts for
8 papers · 1 filter
Entity-based Reinforcement Learning for Autonomous Cyber Defence
Isaac Symes Thompson, Alberto Caron, Chris Hicks +1
A significant challenge for autonomous cyber defence is ensuring a defensive agent's ability to generalise across diverse network topologies and configurations. This capability is…
Mitigating Deep Reinforcement Learning Backdoors in the Neural Activation Space
Sanyam Vyas, Chris Hicks, Vasilios Mavroudis
This paper investigates the threat of backdoors in Deep Reinforcement Learning (DRL) agent policies and proposes a novel method for their detection at runtime. Our study focuses on…
A View on Out-of-Distribution Identification from a Statistical Testing Theory Perspective
Alberto Caron, Chris Hicks, Vasilios Mavroudis
We study the problem of efficiently detecting Out-of-Distribution (OOD) samples at test time in supervised and unsupervised learning contexts. While ML models are typically trained…
Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously
Stephen Pasteris, Chris Hicks, Vasilios Mavroudis +1
We consider the classic problem of online convex optimisation. Whereas the notion of static regret is relevant for stationary problems, the notion of switching regret is more appro…
Extraction Propagation
Stephen Pasteris, Chris Hicks, Vasilios Mavroudis
Running backpropagation end to end on large neural networks is fraught with difficulties like vanishing gradients and degradation. In this paper we present an alternative architect…
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…
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…
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…