Publications

Here is a selection of our recent publications.

2020

“Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations", “D. Chen, M. Davies, and M. Golbabaee", “International conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) vol.12262, https://doi.org/10.1007/978-3-030-59713-9_2", 2020
“Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders", “M. Golbabaee, G. Bounincontri, C. Pirkl, et al.", “arxiv preprint", 2020
Making decisions on subjective data: Aligning statistics with science to improve outcomes on the modified Irwin test, Karen Tse, Oscar Hammar and Beate Ehrhardt, Journal of Pharmacological and Toxicological Methods vol. 99, no. 106595, 2020
Accelerating Variance-Reduced Stochastic Gradient Methods, Derek Driggs, Matthias J. Ehrhardt, Carola-Bibiane Schönlieb, Mathematical Programming, https://doi.org/10.1007/s10107-020-01566-2, 2020

2019

Approximate Bayesian Inference for Geostatistical Generalised Linear Models, Evangelos Evangelou, Foundations of Data Science, 2019
An innovative non-invasive technique for subcutaneous tumour measurements, Juan Delgado San Martin, Beate Ehrhardt, Marcin Paczkowski, Sean Hackett, Andrew Smith, Wajahat Waraich, James Klatzow, Adeala Zabair, Anna Chabokdast, Leonardo Rubio-Navarro, Amar Rahi, and Zena Wilson, PLoS ONE vol. 14, no. 10, 2019
Network modularity in the presence of covariates, Beate Ehrhardt and Patrick J Wolfe, SIAM Rev., 2019
Development of an automated segmentation algorithm to identify bones of the hand, A.M. Rambojun, W. Tillett, N.D.F. Campbell, and T. Shardlow, Annals of the Rheumatic Diseases, vol. 78., 2019
A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models, T. Deveney, E. Mueller, and T. Shardlow", arXiv e-print 1910.01547, 2019
Max-plus Linear Inverse Problems: 2-norm regression and system identification of max-plus linear dynamical systems with Gaussian noise, J. Hook, arXiv e-prints, 2019
Monotonic Gaussian Process Flow, Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek and Neill D. F. Campbell, arXiv e-print, 2019
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures, Andrew Lawrence, Carl Henrik Ek and Neill D. F. Campbell, Int. Conf. on Machine Learning (ICML), 2019
Gaussian Process Latent Variable Alignment Learning, Ieva Kazlauskaite, Carl Henrik Ek and Neill D. F. Campbell, Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 2019

2018

Bridging trees for posterior inference on ancestral recombination graphs, K. Heine, A. Beskos, A. Jasra, D. Balding and M. De Iorio, Proc. Math. Phys. Eng. Sci. vol. 474, no. 2220, 2018
A domain specific language for performance portable molecular dynamics algorithms, William Robert Saunders, James Grant and Eike Hermann Müller, Comput. Phys. Commun. vol. 224, 2018
A stratified age-period-cohort model for spatial heterogeneity in all-cause mortality, Theresa Smith, arXiv e-print 1806.02748, 2018
A low-rank approach to the solution of weak constraint variational data assimilation problems, Melina A Freitag and Daniel L H Green, J. Comput. Phys. vol. 357, 2018
Likelihood Inference for Large Scale Stochastic Blockmodels With Covariates Based on a Divide-and-Conquer Parallelizable Algorithm With Communication, Sandipan Roy, Yves Atchadé and George Michailidis, J. Comput. Graph. Stat., 2018
Approximation and sampling of multivariate probability distributions in the tensor train decomposition, Sergey Dolgov, Karim Anaya-Izquierdo, Colin Fox and Robert Scheichl, arXiv e-print 1810.01212, 2018
Balanced model order reduction for linear random dynamical systems driven by Lévy noise, Martin Redmann and Melina A Freitag, Journal of Computational Dynamics vol. 5, no. 1&2, 2018
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications, A. Chambolle, M. Ehrhardt, P. Richtárik and C. Schönlieb, SIAM J. Optim., 2018
Inexact Gradient Projection and Fast Data Driven Compressed Sensing, M. Golbabaee and M. E. Davies, IEEE Trans. Inf. Theory vol. 64, no. 10, 2018
Dirichlet process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations, W. Del Pozzo, C. P. L. Berry, A. Ghosh, T. S. F. Haines and others, Mon. Not. R. Astron. Soc., 2018
Multilevel Monte Carlo and Improved Timestepping Methods in Atmospheric Dispersion Modelling, Grigoris Katsiolides, Eike H. Mueller, Robert Scheichl, Tony Shardlow, Michael B. Giles and David J. Thomson, J. Computational Physics vol. 354, 2018
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization, Clarice Poon, Nicolas Keriven and Gabriel Peyré, arXiv e-print 1810.03340, 2018
Reconstructing Networks with Unknown and Heterogeneous Errors, Tiago P Peixoto, Phys. Rev. X vol. 8, no. 4, 2018
Sequence Alignment with Dirichlet Process Mixtures, Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek and Neill D. F. Campbell, NeurIPS Workshop on Bayesian Non-Parametrics, 2018
Structured Uncertainty Prediction Networks, Garoe Dorta Perez, Sara Vicente, Lourdes Agapito, Neill D. F. Campbell and Ivor Simpson, IEEE Conf.~on Computer Vision and Pattern Recognition (CVPR), 2018

2017

Sequential empirical Bayes method for filtering dynamic spatiotemporal processes, Evangelos Evangelou and Vasileios Maroulas, Spatial Statistics vol. 21, 2017
Multivariate locally stationary 2D wavelet processes with application to colour texture analysis, Sarah L Taylor, Idris A Eckley and Matthew A Nunes, Stat. Comput. vol. 27, no. 4, 2017
Fluctuations, stability and instability of a distributed particle filter with local exchange, Kari Heine and Nick Whiteley, Stochastic Process. Appl. vol. 127, no. 8, 2017
Change point estimation in high dimensional Markov random-field models, Sandipan Roy, Yves Atchadé and George Michailidis, J. R. Stat. Soc. Series B Stat. Methodol. vol. 79, no. 4, 2017
Max-Plus Algebraic Statistical Leverage Scores, J. Hook, SIAM Journal on Matrix Analysis and Applications vol. 38, no. 4, 2017
Langevin equations for landmark image registration with uncertainty, Stephen Marsland and Tony Shardlow, SIAM Imaging Science vol. 10, 2017
Breaking the coherence barrier: a new theory for compressed sensing, Ben Adcock, Anders C Hansen, Clarice Poon and Bogdan Roman, Forum of Mathematics, Sigma vol. 5, 2017
Towards the Geometry of Model Sensitivity: An Illustration, Karim Anaya-Izquierdo, Frank Critchley, Paul Marriott and Paul Vos, Computational Information Geometry: For Image and Signal Processing, 2017
Latent Gaussian Process Regression, Erik Bodin, Neill D. F. Campbell and Carl Henrik Ek, arXiv e-print 1707.05534, 2017
Latent Structure Learning using Gaussian and Dirichlet Processes, Andrew Lawrence, Carl Henrik Ek and Neill D. F. Campbell, NeurIPS Workshop on Advances in Modelling and Learning Interactions from Complex Data, 2017
Nonparametric Inference for Auto-Encoding Variational Bayes, Erik Bodin, Iman Malik, Carl Henrik Ek and Neill D. F. Campbell, NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2017

2016

Statistical Inference, Learning and Models in Big Data, Beate Franke, Jean-François Plante, Ribana Roscher, En-Shiun Annie Lee, Cathal Smyth, Armin Hatefi, Fuqi Chen, Einat Gil, Alexander Schwing, Alessandro Selvitella, Michael M Hoffman, Roger Grosse, Dieter Hendricks and Nancy Reid, Int. Stat. Rev. vol. 84, no. 3, 2016
Why most decisions are easy in tetris-and perhaps in other sequential decision problems, as well, Özgür Şimşek, Simon Algorta and Amit Kothiyal, Proceedings of Machine Learning Research vol. 48, 2016

2015

Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model, Jonathan W Bartlett, Shaun R Seaman, Ian R White, James R Carpenter and Alzheimer's Disease Neuroimaging Initiative, Stat. Methods Med. Res. vol. 24, no. 4, 2015
Modelling and Prediction of Time Series Arising on a Graph, Matthew A Nunes, Marina I Knight and Guy P Nason, Modeling and Stochastic Learning for Forecasting in High Dimensions, 2015
Restricted Covariance Priors with Applications in Spatial Statistics, Theresa R Smith, Jon Wakefield and Adrian Dobra, Bayesian Anal. vol. 10, no. 4, 2015
Model selection with low complexity priors, Samuel Vaiter, Mohammad Golbabaee, Jalal Fadili and Gabriel Peyré, Inf Inference vol. 4, no. 3, 2015

2014

Improving upon the efficiency of complete case analysis when covariates are MNAR, Jonathan W Bartlett, James R Carpenter, Kate Tilling and Stijn Vansteelandt, Biostatistics vol. 15, no. 4, 2014
Alternating Minimal Energy Methods for Linear Systems in Higher Dimensions, S. Dolgov and D. Savostyanov, SIAM J. Sci. Comput. vol. 36, no. 5, 2014
Joint reconstruction of PET-MRI by exploiting structural similarity, Matthias J Ehrhardt, Kris Thielemans, Luis Pizarro, David Atkinson, Sébastien Ourselin, Brian F Hutton and Simon R Arridge, Inverse Probl. vol. 31, no. 1, 2014
Massively parallel solvers for elliptic partial differential equations in numerical weather and climate prediction, E H Müller and R Scheichl, Quarterly Journal of the Royal, 2014
Active Rare Class Discovery and Classification Using Dirichlet Processes, Tom S F Haines and Tao Xiang, Int. J. Comput. Vis. vol. 106, no. 3, 2014
Hierarchical Block Structures and High-Resolution Model Selection in Large Networks, Tiago P Peixoto, Phys. Rev. X vol. 4, no. 1, 2014

2013

Linear decision rule as aspiration for simple decision heuristics, Özgür Şimşek, Advances in Neural Information Processing Systems 26, 2013
Computational Information Geometry in Statistics: Foundations, Karim Anaya-Izquierdo, Frank Critchley, Paul Marriott and Paul Vos, Lecture Notes in Computer Science, 2013