Publications

Becky Inkster, Ross O'Brien, Emma Selby, Smriti Joshi, Vinod Subramanian, Madhura Kadaba, Knut Schroeder, Suzi Godson, Kerstyn Comley, Sebastian J Vollmer, Bilal A Mateen (2020). Digital Health Management During and Beyond the COVID-19 Pandemic: Opportunities, Barriers, and Recommendations. JMIR Ment Health.

Harrison Wilde, Lucia Lushi Chen, Austin Nguyen, Zoe Kimpel, Joshua Sidgwick, Adolfo De Unanue, Davide Veronese, Bilal Mateen, Rayid Ghani, Sebastian Vollmer (2020). A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services.

(2020). Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ.

Gergo, Gaurav Venkataraman, Harrison Wild, Sebastian J. Vollmer Bohner (2020). coexist.

M B Giles, M B Majka, L Szpruch, S J Vollmer, others (2020). Multi-level Monte Carlo methods for the approximation of invariant measures of stochastic differential equations. Statistics.

J M Dennis, B A Mateen, R Sonabend, others (2020). Diabetes and COVID-19 Related Mortality in the Critical Care Setting: A Real-Time National Cohort Study in England. Available at SSRN.

Bilal A Mateen, Harrison Wilde, John m Dennis, Andrew Duncan, Nicholas John Meyrick Thomas, Andrew P McGovern, Spiros Denaxas, Matt J Keeling, Sebastian J Vollmer (2020). A geotemporal survey of hospital bed saturation across England during the first wave of the COVID-19 Pandemic. medRxiv.

Jackson Gorham, Andrew B Duncan, Sebastian J Vollmer, Lester Mackey (2019). Measuring sample quality with diffusions. Ann. Appl. Probab..

Diego Arenas, Jon Atkins, Claire Austin, David Beavan, Alvaro Cabrejas Egea, Steven Carlysle-Davies, Ian Carter, Rob Clarke, James Cunningham, Tom Doel, Oliver Forrest, Evelina Gabasova, James Geddes, James Hetherington, Radka Jersakova, Franz Kiraly, Catherine Lawrence, Jules Manser, Martin T O'Reilly, James Robinson, Helen Sherwood-Taylor, Serena Tierney, Catalina A Vallejos, Sebastian Vollmer, Kirstie Whitaker (2019). Design choices for productive, secure, data-intensive research at scale in the cloud.

Sergios Agapiou, Gareth O Roberts, Sebastian J Vollmer (2018). Unbiased Monte Carlo: Posterior estimation for intractable/infinite-dimensional models. Bernoulli.

Alexandre Bouchard-Côté, Sebastian J Vollmer, Arnaud Doucet (2018). The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method. J. Am. Stat. Assoc..

J Bierkens, A Bouchard-Côté, A Doucet, others (2018). Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains. Stat. Probab. Lett..

Louis J M Aslett, Tigran Nagapetyan, Sebastian J Vollmer (2017). Multilevel Monte Carlo for Reliability Theory. Reliab. Eng. Syst. Saf..

T Nagapetyan, A B Duncan, L Hasenclever, others (2017). The true cost of stochastic gradient Langevin dynamics. arXiv preprint arXiv.

X Lu, V Perrone, L Hasenclever, others (2017). Relativistic monte carlo. Artif. Intell..

Mikołaj J Kasprzak, Andrew B Duncan, Sebastian J Vollmer, Others (2017). Note on A. Barbour's paper on Stein's method for diffusion approximations. Electron. Commun. Probab..

Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh (2017). Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server. J. Mach. Learn. Res..

Christian Döbler, Robert E Gaunt, Sebastian J Vollmer, Others (2017). An iterative technique for bounding derivatives of solutions of Stein equations. Electron. J. Probab..

Christian Döbler, Robert E Gaunt, Sebastian J Vollmer (2017). An iterative technique for bounding derivatives of solutions of Stein equations. Electron. J. Probab..

Sebastian J Vollmer, Konstantinos C Zygalakis, Yee Whye Teh (2016). Exploration of the (non-)asymptotic bias and variance of stochastic gradient langevin dynamics. J. Mach. Learn. Res..

Yee Whye Teh, Alexandre H Thiery, Sebastian J Vollmer (2016). Consistency and fluctuations for stochastic gradient Langevin dynamics. J. Mach. Learn. Res..

Yee Whye Teh, Alexandre H Thiery, Sebastian J Vollmer (2016). Consistency and fluctuations for stochastic gradient Langevin dynamics. J. Mach. Learn. Res..

Sebastian J Vollmer (2015). Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors. SIAM/ASA J. Uncertainty Quantification.

M Hairer, A M Stuart, S J Vollmer (2014). Spectral gaps for a Metropolis--Hastings algorithm in infinite dimensions. The Annals of Applied.

M Welling, Y W Teh, C Andrieu, J Kominiarczuk, others (2014). Bayesian inference with big data: a snapshot from a workshop. pdfs.semanticscholar.org.

S J Vollmer (2013). Posterior consistency for Bayesian inverse problems through stability and regression results. Inverse Probl..

Sebastian J Vollmer (2013). Efficient MCMC and Posterior Consistency for Bayesian Inverse Problems.