Posts by Collection

Information-theoretic State Variable Selection for Reinforcement Learning

Published in arXiv preprint, 2024

This paper introduces the Transfer Entropy Redundancy Criterion (TERC), an information-theoretic approach for identifying optimal state variables in RL that provably excludes variables with no effect on agent performance.

Recommended citation: Westphal, C., Hailes, S., & Musolesi, M. (2024). Information-theoretic State Variable Selection for Reinforcement Learning. arXiv preprint arXiv:2401.11512.
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Partial Information Decomposition for Data Interpretability and Feature Selection

Published in arXiv preprint, 2024

This paper introduces PIDF, a novel approach that simultaneously addresses data interpretability and feature selection by evaluating each feature using three metrics: mutual information, synergistic contribution, and redundancy.

Recommended citation: Westphal, C., Hailes, S., & Musolesi, M. (2024). Partial Information Decomposition for Data Interpretability and Feature Selection. arXiv preprint arXiv:2405.19212.
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Mutual Information Preserving Neural Network Pruning

Published in arXiv preprint, 2024

This paper proposes Mutual Information Preserving Pruning (MIPP), a structured activation-based pruning method that selects nodes to conserve mutual information shared between activations of adjacent layers.

Recommended citation: Westphal, C., Hailes, S., & Musolesi, M. (2024). Mutual Information Preserving Neural Network Pruning. arXiv preprint arXiv:2411.00147.
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Feature Selection for Network Intrusion Detection

Published in Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025

This paper presents FSNID, a novel information-theoretic method that facilitates the exclusion of non-informative features when detecting network intrusions, selecting a significantly reduced feature set while maintaining performance.

Recommended citation: Westphal, C., Hailes, S., & Musolesi, M. (2025). Feature Selection for Network Intrusion Detection. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 1599-1610).
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A Generalized Information Bottleneck Theory of Deep Learning

Published in arXiv preprint, 2025

This paper introduces a Generalized Information Bottleneck framework that reconceptualizes the original Information Bottleneck principle through synergy—information accessible only via joint feature processing.

Recommended citation: Westphal, C., Hailes, S., & Musolesi, M. (2025). A Generalized Information Bottleneck Theory of Deep Learning. arXiv preprint arXiv:2509.26327.
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Partial Information Decomposition for Data Interpretability and Feature Selection

Published: April 04, 2025

I will present my work on Partial Information Decomposition (PIDF) as part of the opening session of AiStats 2025. This will cover novel methods for quantifying redundant, unique, and synergistic information in high-dimensional datasets, with applications to interpretable machine learning and feature selection.

Feature Selection for Network Intrusion Detection: An Information-Theoretic Approach

Published: May 08, 2025

I will present my research on information-theoretic feature selection for network intrusion detection systems. This work introduces novel methods to identify minimal yet maximally discriminative features in cybersecurity datasets, significantly reducing computational overhead while maintaining state-of-the-art detection accuracy. The talk will include: