most citedFeature Selection for Network Intrusion Detection

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cs.LG2025

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…

cs.LG2025

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…

cs.LG2025

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…

cs.LG20241 cited

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…

cs.LG2024

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…

cs.LG2024

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…