Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
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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talks
Partial Information Decomposition for Data Interpretability and Feature Selection
Published:
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:
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: