Simulation of Improper Complex-Valued Sequences. - Patrick Rubin-Delanchy; Andrew T. Walden (IEEE Trans. Signal Process., 2007)

Kinematics of Complex-Valued Time Series. - Patrick Rubin-Delanchy; Andrew T. Walden (IEEE Trans. Signal Process., 2008)

On Testing for Impropriety of Complex-Valued Gaussian Vectors. - Andrew T. Walden; Patrick Rubin-Delanchy (IEEE Trans. Signal Process., 2009)

Filtering Automated Polling Traffic in Computer Network Flow Data. - Nick Heard; Patrick Rubin-Delanchy; Daniel J. Lawson (JISIC, 2014)

Statistical Frameworks for Detecting Tunnelling in Cyber Defence Using Big Data. - Daniel J. Lawson; Patrick Rubin-Delanchy; Nicholas A. Heard; Niall M. Adams (JISIC, 2014)

Three Statistical Approaches to Sessionizing Network Flow Data. - Patrick Rubin-Delanchy; Daniel J. Lawson; Melissa J. Turcotte; Nicholas A. Heard; Niall M. Adams (JISIC, 2014)

Network-wide anomaly detection via the Dirichlet process. - Nick Heard; Patrick Rubin-Delanchy (ISI, 2016)

Disassortativity of computer networks. - Patrick Rubin-Delanchy; Niall M. Adams; Nicholas A. Heard (ISI, 2016)

A statistical interpretation of spectral embedding: the generalised random dot product graph. - Patrick Rubin-Delanchy; Carey E. Priebe; Minh Tang; Joshua Cape (CoRR, 2017)

Spectral clustering in the weighted stochastic block model. - Ian Gallagher; Anna Bertiger; Carey E. Priebe; Patrick Rubin-Delanchy (CoRR, 2019)

Manifold structure in graph embeddings. - Patrick Rubin-Delanchy (NeurIPS, 2020)

The multilayer random dot product graph. - Andrew Jones; Patrick Rubin-Delanchy (CoRR, 2020)

Spectral embedding for dynamic networks with stability guarantees. - Ian Gallagher; Andrew Jones; Patrick Rubin-Delanchy (NeurIPS, 2021)

Matrix factorisation and the interpretation of geodesic distance. - Nick Whiteley; Annie Gray; Patrick Rubin-Delanchy (NeurIPS, 2021)

Statistical exploration of the Manifold Hypothesis. - Nick Whiteley; Annie Gray; Patrick Rubin-Delanchy (CoRR, 2022)

Spectral Clustering on Spherical Coordinates Under the Degree-Corrected Stochastic Blockmodel. - Francesco Sanna Passino; Nicholas A. Heard; Patrick Rubin-Delanchy (Technometrics, 2022)

Implications of sparsity and high triangle density for graph representation learning. - Hannah Sansford; Alexander Modell; Nick Whiteley; Patrick Rubin-Delanchy (AISTATS, 2023)

Hierarchical clustering with dot products recovers hidden tree structure. - Annie Gray; Alexander Modell; Patrick Rubin-Delanchy; Nick Whiteley (NeurIPS, 2023)

Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks. - Alexander Modell; Ian Gallagher; Emma Ceccherini; Nick Whiteley; Patrick Rubin-Delanchy (NeurIPS, 2023)

A Simple and Powerful Framework for Stable Dynamic Network Embedding. - Ed Davis; Ian Gallagher; Daniel John Lawson; Patrick Rubin-Delanchy (CoRR, 2023)

Valid Conformal Prediction for Dynamic GNNs. - Ed Davis; Ian Gallagher; Daniel John Lawson; Patrick Rubin-Delanchy (ICLR, 2025)

How high is 'high'? Rethinking the roles of dimensionality in topological data analysis and manifold learning. - Hannah Sansford; Nick Whiteley; Patrick Rubin-Delanchy (CoRR, 2025)

The Origins of Representation Manifolds in Large Language Models. - Alexander Modell; Patrick Rubin-Delanchy; Nick Whiteley (CoRR, 2025)