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Sebastian J. Vollmer
Sebastian J. Vollmer
Verified email at cs.uni-kl.de - Homepage
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Cited by
Year
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
4492020
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
3702020
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
2342018
Consistency and fluctuations for stochastic gradient Langevin dynamics
YW Teh, AH Thiery, SJ Vollmer
Journal of Machine Learning Research 17, 2016
2222016
Consistency and fluctuations for stochastic gradient Langevin dynamics
YW Teh, AH Thiery, SJ Vollmer
Journal of Machine Learning Research 17, 2016
2222016
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
2132020
Spectral gaps for a Metropolis–Hastings algorithm in infinite dimensions
M Hairer, AM Stuart, SJ Vollmer
1842014
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
1792021
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
1262021
Measuring sample quality with diffusions
J Gorham, AB Duncan, SJ Vollmer, L Mackey
The Annals of Applied Probability 29 (5), 2884-2928, 2019
1032019
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
1002016
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
72*2017
Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed
Nature Medicine 25 (10), 1467-1468, 2019
662019
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
582018
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
572021
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
542020
The true cost of stochastic gradient Langevin dynamics
T Nagapetyan, AB Duncan, L Hasenclever, SJ Vollmer, L Szpruch, ...
arXiv preprint arXiv:1706.02692, 2017
512017
Multilevel Monte Carlo for reliability theory
LJM Aslett, T Nagapetyan, SJ Vollmer
Reliability Engineering & System Safety 165, 188-196, 2017
492017
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
432020
Relativistic monte carlo
X Lu, V Perrone, L Hasenclever, YW Teh, S Vollmer
Artificial Intelligence and Statistics, 1236-1245, 2017
432017
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Articles 1–20