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2020 – today
- 2023
[c8]Hannah Sansford, Alexander Modell, Nick Whiteley, Patrick Rubin-Delanchy:
Implications of sparsity and high triangle density for graph representation learning. AISTATS 2023: 5449-5473- [c7]Annie Gray, Alexander Modell, Patrick Rubin-Delanchy, Nick Whiteley:
Hierarchical clustering with dot products recovers hidden tree structure. NeurIPS 2023 - [c6]Alexander Modell, Ian Gallagher, Emma Ceccherini, Nick Whiteley, Patrick Rubin-Delanchy:
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks. NeurIPS 2023 - [i11]Annie Gray, Alexander Modell, Patrick Rubin-Delanchy, Nick Whiteley:
Hierarchical clustering with dot products recovers hidden tree structure. CoRR abs/2305.15022 (2023) - [i10]Alexander Modell, Ian Gallagher, Emma Ceccherini, Nick Whiteley, Patrick Rubin-Delanchy:
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks. CoRR abs/2306.06155 (2023) - 2022
- [j9]Lorenzo Rimella, Nick Whiteley:
Exploiting locality in high-dimensional Factorial hidden Markov models. J. Mach. Learn. Res. 23: 4:1-4:34 (2022) - [i9]Michael Whitehouse, Nick Whiteley, Lorenzo Rimella:
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods. CoRR abs/2205.13602 (2022) - [i8]Nick Whiteley
, Annie Gray, Patrick Rubin-Delanchy:
Statistical exploration of the Manifold Hypothesis. CoRR abs/2208.11665 (2022) - [i7]Hannah Sansford, Alexander Modell, Nick Whiteley, Patrick Rubin-Delanchy:
Implications of sparsity and high triangle density for graph representation learning. CoRR abs/2210.15277 (2022) - 2021
- [j8]Lewis J. Rendell, Adam M. Johansen, Anthony Lee, Nick Whiteley:
Global Consensus Monte Carlo. J. Comput. Graph. Stat. 30(2): 249-259 (2021) - [c5]Nick Whiteley, Lorenzo Rimella:
Inference in Stochastic Epidemic Models via Multinomial Approximations. AISTATS 2021: 1297-1305 - [c4]Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy:
Matrix factorisation and the interpretation of geodesic distance. NeurIPS 2021: 24-38 - [i6]Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy:
Matrix factorisation and the interpretation of geodesic distance. CoRR abs/2106.01260 (2021) - 2020
- [i5]Lorenzo Rimella, Nick Whiteley:
Dynamic Bayesian Neural Networks. CoRR abs/2004.06963 (2020) - [i4]Nick Whiteley, Lorenzo Rimella:
Inference in Stochastic Epidemic Models via Multinomial Approximations. CoRR abs/2006.13700 (2020)
2010 – 2019
- 2019
- [i3]Lorenzo Rimella, Nick Whiteley:
Exploiting locality in high-dimensional factorial hidden Markov models. CoRR abs/1902.01639 (2019) - 2018
- [i2]Nick Whiteley:
The Viterbi process, decay-convexity and parallelized maximum a-posteriori estimation. CoRR abs/1810.04115 (2018) - 2017
- [j7]Mathieu Gerber, Nick Whiteley:
Stability with respect to initial conditions in V-norm for nonlinear filters with ergodic observations. J. Appl. Probab. 54(1): 118-133 (2017) - [j6]Nick Whiteley, Nikolas Kantas:
Calculating Principal Eigen-Functions of Non-Negative Integral Kernels: Particle Approximations and Applications. Math. Oper. Res. 42(4): 1007-1034 (2017) - [j5]Cian O'Donnell, J. Tiago Gonçalves, Nick Whiteley, Carlos Portera-Cailliau, Terrence J. Sejnowski:
The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data. Neural Comput. 29(1): 50-93 (2017) - 2016
- [j4]Marc Box, Matt W. Jones, Nick Whiteley:
A hidden Markov model for decoding and the analysis of replay in spike trains. J. Comput. Neurosci. 41(3): 339-366 (2016) - [j3]Anthony Lee, Nick Whiteley:
Forest resampling for distributed sequential Monte Carlo. Stat. Anal. Data Min. 9(4): 230-248 (2016) - [j2]Juha Ala-Luhtala, Nick Whiteley, Kari Heine, Robert Piché:
An Introduction to Twisted Particle Filters and Parameter Estimation in Non-Linear State-Space Models. IEEE Trans. Signal Process. 64(18): 4875-4890 (2016) - 2014
- [c3]Marcelo Pereyra, Nick Whiteley, Christophe Andrieu, Jean-Yves Tourneret:
Maximum marginal likelihood estimation of the granularity coefficient of a Potts-Markov random field within an MCMC algorithm. SSP 2014: 121-124 - 2013
- [j1]Sumeetpal S. Singh, Nicolas Chopin, Nick Whiteley:
Bayesian Learning of Noisy Markov Decision Processes. ACM Trans. Model. Comput. Simul. 23(1): 4:1-4:25 (2013) - 2012
- [i1]Sumeetpal S. Singh, Nicolas Chopin, Nick Whiteley:
Bayesian learning of noisy Markov decision processes. CoRR abs/1211.5901 (2012)
2000 – 2009
- 2007
- [c2]Nick Whiteley, A. Taylan Cemgil, Simon J. Godsill:
Sequential Inference of Rhythmic Structure in Musical Audio. ICASSP (4) 2007: 1321-1324 - 2006
- [c1]Nick Whiteley, Ali Taylan Cemgil, Simon J. Godsill:
Bayesian Modelling of Temporal Structure in Musical Audio. ISMIR 2006: 29-34
Coauthor Index
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