Sebastian J. Vollmer
Sebastian J. Vollmer
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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
X Liu, SC Rivera, D Moher, MJ Calvert, AK Denniston, H Ashrafian, ...
The Lancet Digital Health 2 (10), e537-e548, 2020
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
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
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, A Thiéry, SJ Vollmer
Journal of Machine Learning Research 17 (7), 2016
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), 209-214, 2021
Spectral gaps for a Metropolis–Hastings algorithm in infinite dimensions
M Hairer, AM Stuart, SJ Vollmer
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
Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
V Sounderajah, H Ashrafian, RM Golub, S Shetty, J De Fauw, L Hooft, ...
BMJ open 11 (6), e047709, 2021
Measuring sample quality with diffusions
J Gorham, AB Duncan, SJ Vollmer, L Mackey
The Annals of Applied Probability 29 (5), 2884-2928, 2019
Exploration of the (non-) asymptotic bias and variance of stochastic gradient Langevin dynamics
SJ Vollmer, KC Zygalakis, YW Teh
Journal of Machine Learning Research 17 (159), 1-48, 2016
Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed
Nature Medicine 25 (10), 1467-1468, 2019
Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server
L Hasenclever, S Webb, T Lienart, S Vollmer, B Lakshminarayanan, ...
Journal of Machine Learning Research 18 (106), 1-37, 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
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
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
MLJ: A Julia package for composable machine learning
AD Blaom, F Kiraly, T Lienart, Y Simillides, D Arenas, SJ Vollmer
arXiv preprint arXiv:2007.12285, 2020
Posterior consistency for Bayesian inverse problems through stability and regression results
SJ Vollmer
Inverse Problems 29 (12), 125011, 2013
Multilevel Monte Carlo for reliability theory
LJM Aslett, T Nagapetyan, SJ Vollmer
Reliability Engineering & System Safety 165, 188-196, 2017
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