Bayesian Modelling of Temporal Structure in Musical Audio. - Nick Whiteley; Ali Taylan Cemgil; Simon J. Godsill (ISMIR, 2006)

Sequential Inference of Rhythmic Structure in Musical Audio. - Nick Whiteley; A. Taylan Cemgil; Simon J. Godsill (ICASSP (4), 2007)

Bayesian Learning of Noisy Markov Decision Processes. - Sumeetpal S. Singh; Nicolas Chopin; Nick Whiteley (ACM Trans. Model. Comput. Simul., 2013)

Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm. - Marcelo Pereyra; Nick Whiteley; Christophe Andrieu; Jean-Yves Tourneret (SSP, 2014)

A hidden Markov model for decoding and the analysis of replay in spike trains. - Marc Box; Matt W. Jones; Nick Whiteley (J. Comput. Neurosci., 2016)

Forest resampling for distributed sequential Monte Carlo. - Anthony Lee; Nick Whiteley (Stat. Anal. Data Min., 2016)

An Introduction to Twisted Particle Filters and Parameter Estimation in Non-Linear State-Space Models. - Juha Ala-Luhtala; Nick Whiteley; Kari Heine; Robert Piché (IEEE Trans. Signal Process., 2016)

Stability with respect to initial conditions in V-norm for nonlinear filters with ergodic observations. - Mathieu Gerber; Nick Whiteley (J. Appl. Probab., 2017)

Calculating Principal Eigen-Functions of Non-Negative Integral Kernels: Particle Approximations and Applications. - Nick Whiteley; Nikolas Kantas (Math. Oper. Res., 2017)

The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data. - Cian O'Donnell; J. Tiago Gonçalves; Nick Whiteley; Carlos Portera-Cailliau; Terrence J. Sejnowski (Neural Comput., 2017)

The Viterbi process, decay-convexity and parallelized maximum a-posteriori estimation. - Nick Whiteley (CoRR, 2018)

Dynamic Bayesian Neural Networks. - Lorenzo Rimella; Nick Whiteley (CoRR, 2020)

Inference in Stochastic Epidemic Models via Multinomial Approximations. - Nick Whiteley; Lorenzo Rimella (AISTATS, 2021)

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

Global Consensus Monte Carlo. - Lewis J. Rendell; Adam M. Johansen; Anthony Lee; Nick Whiteley (J. Comput. Graph. Stat., 2021)

Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods. - Michael Whitehouse; Nick Whiteley; Lorenzo Rimella (CoRR, 2022)

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

Exploiting locality in high-dimensional Factorial hidden Markov models. - Lorenzo Rimella; Nick Whiteley (J. Mach. Learn. Res., 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)

Conditional Distribution Compression via the Kernel Conditional Mean Embedding. - Dominic Broadbent; Nick Whiteley; Robert Allison; Tom Lovett (CoRR, 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)