2 citations · 10 across the 14 of their papers we have counts for
14 papers
The Role of Network Topology and Opponent Information in Shaping Cooperation in Multi-Agent Reinforcement Learning Systems
Seongho Son, Stephen Hailes, Mirco Musolesi
Several works have investigated the influence of graph topology on cooperation among artificial agents, while the majority of the literature has focused on modelling agents' adapta…
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…
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…
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…
Feature Selection for Network Intrusion Detection
Charles Westphal, Stephen Hailes, Mirco Musolesi
Network Intrusion Detection (NID) remains a key area of research within the information security community, while also being relevant to Machine Learning (ML) practitioners. The la…
Mutual Information Preserving Neural Network Pruning
Charles Westphal, Stephen Hailes, Mirco Musolesi
Pruning has emerged as the primary approach used to limit the resource requirements of large neural networks (NNs). Since the proposal of the lottery ticket hypothesis, researchers…
Moral Alignment for LLM Agents
Elizaveta Tennant, Stephen Hailes, Mirco Musolesi
Decision-making agents based on pre-trained Large Language Models (LLMs) are increasingly being deployed across various domains of human activity. While their applications are curr…
Large Language Models are Effective Priors for Causal Graph Discovery
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Causal structure discovery from observations can be improved by integrating background knowledge provided by an expert to reduce the hypothesis space. Recently, Large Language Mode…
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…
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Graphs are a natural representation for systems based on relations between connected entities. Combinatorial optimization problems, which arise when considering an objective functi…
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…
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…
Hybrid Approaches for Moral Value Alignment in AI Agents: a Manifesto
Elizaveta Tennant, Stephen Hailes, Mirco Musolesi
Increasing interest in ensuring the safety of next-generation Artificial Intelligence (AI) systems calls for novel approaches to embedding morality into autonomous agents. This goa…
Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Identifying causal structure is central to many fields ranging from strategic decision-making to biology and economics. In this work, we propose CD-UCT, a model-based reinforcement…