Nick Whiteley - RankMe

35 papers A* 3A 2Journal 27Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Uncovering Locally Low-dimensional Structure in Networks by Locally Optimal Spectral Embedding. CoRR Hannah Sansford, Nick Whiteley, Patrick Rubin-Delanchy
2025	J	`jnl`	Bilateral Distribution Compression: Reducing Both Data Size and Dimensionality. CoRR Dominic Broadbent, Nick Whiteley, Robert Allison, Tom Lovett
2025	J	`jnl`	Conditional Distribution Compression via the Kernel Conditional Mean Embedding. CoRR Dominic Broadbent, Nick Whiteley, Robert Allison, Tom Lovett
2025	J	`jnl`	Generalisation and benign over-fitting for linear regression onto random functional covariates. CoRR Andrew Jones, Nick Whiteley
2025	J	`jnl`	Hidden Markov Neural Networks. Entropy Lorenzo Rimella, Nick Whiteley
2025	J	`jnl`	How high is 'high'? Rethinking the roles of dimensionality in topological data analysis and manifold learning. CoRR Hannah Sansford, Nick Whiteley, Patrick Rubin-Delanchy
2025	J	`jnl`	The Origins of Representation Manifolds in Large Language Models. CoRR Alexander Modell, Patrick Rubin-Delanchy, Nick Whiteley
2023	A*	`conf`	Hierarchical clustering with dot products recovers hidden tree structure. NeurIPS Annie Gray, Alexander Modell, Patrick Rubin-Delanchy, Nick Whiteley
2023	J	`jnl`	Hierarchical clustering with dot products recovers hidden tree structure. CoRR Annie Gray, Alexander Modell, Patrick Rubin-Delanchy, Nick Whiteley
2023	A	`conf`	Implications of sparsity and high triangle density for graph representation learning. AISTATS Hannah Sansford, Alexander Modell, Nick Whiteley, Patrick Rubin-Delanchy
2023	A*	`conf`	Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks. NeurIPS Alexander Modell, Ian Gallagher, Emma Ceccherini, Nick Whiteley, Patrick Rubin-Delanchy
2023	J	`jnl`	Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks. CoRR Alexander Modell, Ian Gallagher, Emma Ceccherini, Nick Whiteley, Patrick Rubin-Delanchy
2022	J	`jnl`	Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods. CoRR Michael Whitehouse, Nick Whiteley, Lorenzo Rimella
2022	J	`jnl`	Exploiting locality in high-dimensional Factorial hidden Markov models. J. Mach. Learn. Res. Lorenzo Rimella, Nick Whiteley
2022	J	`jnl`	Implications of sparsity and high triangle density for graph representation learning. CoRR Hannah Sansford, Alexander Modell, Nick Whiteley, Patrick Rubin-Delanchy
2022	J	`jnl`	Statistical exploration of the Manifold Hypothesis. CoRR Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy
2021	J	`jnl`	Global Consensus Monte Carlo. J. Comput. Graph. Stat. Lewis J. Rendell, Adam M. Johansen, Anthony Lee, Nick Whiteley
2021	A	`conf`	Inference in Stochastic Epidemic Models via Multinomial Approximations. AISTATS Nick Whiteley, Lorenzo Rimella
2021	A*	`conf`	Matrix factorisation and the interpretation of geodesic distance. NeurIPS Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy
2021	J	`jnl`	Matrix factorisation and the interpretation of geodesic distance. CoRR Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy
2020	J	`jnl`	Dynamic Bayesian Neural Networks. CoRR Lorenzo Rimella, Nick Whiteley
2020	J	`jnl`	Inference in Stochastic Epidemic Models via Multinomial Approximations. CoRR Nick Whiteley, Lorenzo Rimella
2019	J	`jnl`	Exploiting locality in high-dimensional factorial hidden Markov models. CoRR Lorenzo Rimella, Nick Whiteley
2018	J	`jnl`	The Viterbi process, decay-convexity and parallelized maximum a-posteriori estimation. CoRR Nick Whiteley
2017	J	`jnl`	Calculating Principal Eigen-Functions of Non-Negative Integral Kernels: Particle Approximations and Applications. Math. Oper. Res. Nick Whiteley, Nikolas Kantas
2017	J	`jnl`	Stability with respect to initial conditions in V-norm for nonlinear filters with ergodic observations. J. Appl. Probab. Mathieu Gerber, Nick Whiteley
2017	J	`jnl`	The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data. Neural Comput. Cian O'Donnell, J. Tiago Gonçalves, Nick Whiteley, Carlos Portera-Cailliau, Terrence J. Sejnowski
2016	J	`jnl`	A hidden Markov model for decoding and the analysis of replay in spike trains. J. Comput. Neurosci. Marc Box, Matt W. Jones, Nick Whiteley
2016	J	`jnl`	An Introduction to Twisted Particle Filters and Parameter Estimation in Non-Linear State-Space Models. IEEE Trans. Signal Process. Juha Ala-Luhtala, Nick Whiteley, Kari Heine, Robert Piché
2016	J	`jnl`	Forest resampling for distributed sequential Monte Carlo. Stat. Anal. Data Min. Anthony Lee, Nick Whiteley
2014	—	`conf`	Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm. SSP Marcelo Pereyra, Nick Whiteley, Christophe Andrieu, Jean-Yves Tourneret
2013	J	`jnl`	Bayesian Learning of Noisy Markov Decision Processes. ACM Trans. Model. Comput. Simul. Sumeetpal S. Singh, Nicolas Chopin, Nick Whiteley
2012	J	`jnl`	Bayesian learning of noisy Markov decision processes CoRR Sumeetpal S. Singh, Nicolas Chopin, Nick Whiteley
2007	—	`conf`	Sequential Inference of Rhythmic Structure in Musical Audio. ICASSP (4) Nick Whiteley, A. Taylan Cemgil, Simon J. Godsill
2006	—	`conf`	Bayesian Modelling of Temporal Structure in Musical Audio. ISMIR Nick Whiteley, Ali Taylan Cemgil, Simon J. Godsill