Sebastian J. Vollmer
Sebastian J. Vollmer
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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
SC Rivera, X Liu, AW Chan, AK Denniston, MJ Calvert, H Ashrafian, ...
The Lancet Digital Health 2 (10), e549-e560, 2020
The bouncy particle sampler: A nonreversible rejection-free Markov chain Monte Carlo method
A Bouchard-Côté, SJ Vollmer, A Doucet
Journal of the American Statistical Association 113 (522), 855-867, 2018
Consistency and fluctuations for stochastic gradient Langevin dynamics
YW Teh, AH Thiery, SJ Vollmer
Journal of Machine Learning Research 17, 2016
Consistency and fluctuations for stochastic gradient Langevin dynamics
YW Teh, AH Thiery, SJ Vollmer
Journal of Machine Learning Research 17, 2016
Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness
S Vollmer, BA Mateen, G Bohner, FJ Király, R Ghani, P Jonsson, ...
bmj 368, 2020
Spectral gaps for a Metropolis–Hastings algorithm in infinite dimensions
M Hairer, AM Stuart, SJ Vollmer
The Annals of Applied Probability 24 (6), 2455-2490, 2014
Improving survival of critical care patients with coronavirus disease 2019 in England: a national cohort study, March to June 2020
JM Dennis, AP McGovern, SJ Vollmer, BA Mateen
Critical care medicine 49 (2), 209, 2021
Type 2 diabetes and COVID-19–Related mortality in the critical care setting: a national cohort study in England, March–July 2020
JM Dennis, BA Mateen, R Sonabend, NJ Thomas, KA Patel, AT Hattersley, ...
Diabetes care 44 (1), 50-57, 2021
Exploration of the (non-) asymptotic bias and variance of stochastic gradient Langevin dynamics
SJ Vollmer, KC Zygalakis, YW Teh
The Journal of Machine Learning Research 17 (1), 5504-5548, 2016
Measuring sample quality with diffusions
J Gorham, AB Duncan, SJ Vollmer, L Mackey
The Annals of Applied Probability 29 (5), 2884-2928, 2019
Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server
L Hasenclever, S Webb, T Lienart, S Vollmer, B Lakshminarayanan, ...
The Journal of Machine Learning Research 18 (1), 3744-3780, 2017
Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed
Nature Medicine 25 (10), 1467-1468, 2019
Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains
J Bierkens, A Bouchard-Côté, A Doucet, AB Duncan, P Fearnhead, ...
Statistics & Probability Letters 136, 148-154, 2018
The true cost of stochastic gradient Langevin dynamics
T Nagapetyan, AB Duncan, L Hasenclever, SJ Vollmer, L Szpruch, ...
arXiv preprint arXiv:1706.02692, 2017
Multilevel Monte Carlo for reliability theory
LJM Aslett, T Nagapetyan, SJ Vollmer
Reliability Engineering & System Safety 165, 188-196, 2017
Digital health management during and beyond the COVID-19 pandemic: opportunities, barriers, and recommendations
B Inkster, R O’Brien, E Selby, S Joshi, V Subramanian, M Kadaba, ...
JMIR Mental Health 7 (7), e19246, 2020
Relativistic monte carlo
X Lu, V Perrone, L Hasenclever, YW Teh, S Vollmer
Artificial Intelligence and Statistics, 1236-1245, 2017
Posterior consistency for Bayesian inverse problems through stability and regression results
SJ Vollmer
Inverse Problems 29 (12), 125011, 2013
The association between mechanical ventilator compatible bed occupancy and mortality risk in intensive care patients with COVID-19: a national retrospective cohort study
H Wilde, T Mellan, I Hawryluk, JM Dennis, S Denaxas, C Pagel, A Duncan, ...
BMC medicine 19 (1), 1-12, 2021
Improving the quality of machine learning in health applications and clinical research
BA Mateen, J Liley, AK Denniston, CC Holmes, SJ Vollmer
Nature Machine Intelligence 2 (10), 554-556, 2020
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