9 papers
TERC: A Transfer Entropy Redundancy Criterion for State Variable Selection in Reinforcement Learning
Charles Westphal, Stephen Hailes, Mirco Musolesi
Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL). These variables must efficiently capture the information n…
Towards Trustworthy Wi-Fi CSI-based Sensing: Systematic Evaluation of Adversarial Robustness
Shreevanth Krishnaa Gopalakrishnan, Stephen Hailes
Machine learning drives Channel State Information (CSI)-based human sensing in modern wireless networks, enabling applications like device-free human activity recognition (HAR) and…
Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents
Elizaveta Tennant, Stephen Hailes, Mirco Musolesi
Growing concerns about safety and alignment of AI systems highlight the importance of embedding moral capabilities in artificial agents: a promising solution is the use of learning…
A Generalized Information Bottleneck Theory of Deep Learning
Charles Westphal, Stephen Hailes, Mirco Musolesi
The Information Bottleneck (IB) principle offers a compelling theoretical framework to understand how neural networks (NNs) learn. However, its practical utility has been constrain…
Partial Information Decomposition for Data Interpretability and Feature Selection
Charles Westphal, Stephen Hailes, Mirco Musolesi
In this paper, we introduce Partial Information Decomposition of Features (PIDF), a new paradigm for simultaneous data interpretability and feature selection. Contrary to tradition…
Opponent Shaping in LLM Agents
Marta Emili Garcia Segura, Stephen Hailes, Mirco Musolesi
Large Language Models (LLMs) are increasingly being deployed as autonomous agents in real-world environments. As these deployments scale, multi-agent interactions become inevitable…