Sebastian J. Vollmer

Publication Activity (10 Years)

Years Active: 2015-2023
Publications (10 Years): 31

Top Topics

Machine Learning

Bayesian Learning

Monte Carlo Methods

Top Venues

J. Mach. Learn. Res.

Publications

Florian Pfisterer, Siyi Wei, Sebastian J. Vollmer, Michel Lang, Bernd Bischl
Fairness Audits and Debiasing Using \pkg{mlr3fairness}. R J. 15 (1) (2023)
Tobias Schröder, Zijing Ou, Jen Ning Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew B. Duncan
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. CoRR (2023)
Tobias Schröder, Zijing Ou, Jen Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew Duncan
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. NeurIPS (2023)
Ashwini Venkatasubramaniam, Bilal A. Mateen, Beverley M. Shields, Andrew T. Hattersley, Angus G. Jones, Sebastian J. Vollmer, John M. Dennis
Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine. BMC Medical Informatics Decis. Mak. 23 (1) (2023)
Jen Ning Lim, Sebastian J. Vollmer, Lorenz Wolf, Andrew Duncan
Energy-Based Models for Functional Data using Path Measure Tilting. AISTATS (2023)
Raphael Sonabend, Andreas Bender, Sebastian J. Vollmer
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures. Bioinform. 38 (17) (2022)
Jen Ning Lim, Sebastian J. Vollmer, Lorenz Wolf, Andrew Duncan
F-EBM: Energy Based Learning of Functional Data. CoRR (2022)
Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sebastian J. Vollmer
Flexible Group Fairness Metrics for Survival Analysis. CoRR (2022)
Sahra Ghalebikesabi, Harry Wilde, Jack Jewson, Arnaud Doucet, Sebastian J. Vollmer, Chris C. Holmes
Mitigating statistical bias within differentially private synthetic data. UAI (2022)
Raphael Sonabend, Andreas Bender, Sebastian J. Vollmer
Evaluation of survival distribution predictions with discrimination measures. CoRR (2021)
Harrison Wilde, Jack Jewson, Sebastian J. Vollmer, Chris C. Holmes
Foundations of Bayesian Learning from Synthetic Data. AISTATS (2021)
Sahra Ghalebikesabi, Harrison Wilde, Jack Jewson, Arnaud Doucet, Sebastian J. Vollmer, Chris C. Holmes
Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale. CoRR (2021)
James Liley, Samuel R. Emerson, Bilal A. Mateen, Catalina A. Vallejos, Louis J. M. Aslett, Sebastian J. Vollmer
Model updating after interventions paradoxically introduces bias. AISTATS (2021)
Harrison Wilde, Jack Jewson, Sebastian J. Vollmer, Chris C. Holmes
Foundations of Bayesian Learning from Synthetic Data. CoRR (2020)
Michael B. Giles, Mateusz B. Majka, Lukasz Szpruch, Sebastian J. Vollmer, Konstantinos C. Zygalakis
Multi-level Monte Carlo methods for the approximation of invariant measures of stochastic differential equations. Stat. Comput. 30 (3) (2020)
Bilal A. Mateen, James Liley, Alastair K. Denniston, Chris C. Holmes, Sebastian J. Vollmer
Improving the quality of machine learning in health applications and clinical research. Nat. Mach. Intell. 2 (10) (2020)
Ashrya Agrawal, Florian Pfisterer, Bernd Bischl, Jiahao Chen, Srijan Sood, Sameena Shah, Francois Buet-Golfouse, Bilal A. Mateen, Sebastian J. Vollmer
Debiasing classifiers: is reality at variance with expectation? CoRR (2020)
Anthony D. Blaom, Franz J. Király, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer
MLJ: A Julia package for composable Machine Learning. CoRR (2020)
Anthony D. Blaom, Sebastian J. Vollmer
Flexible model composition in machine learning and its implementation in MLJ. CoRR (2020)
James Liley, Samuel R. Emerson, Bilal A. Mateen, Catalina A. Vallejos, Louis J. M. Aslett, Sebastian J. Vollmer
Model updating after interventions paradoxically introduces bias. CoRR (2020)
Anthony D. Blaom, Franz J. Király, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer
MLJ: A Julia package for composable machine learning. J. Open Source Softw. 5 (55) (2020)
Diego Arenas, Jon Atkins, Clare Austin, David Beavan, Alvaro Cabres Egea, Stephen Carlysle-Davies, Ian Carter, Rob Clarke, James Cunningham, Tom Doel, Oliver Forrest, Evelina Gabasova, James Geddes, James Hetherington, Radka Jersakova, Franz J. Király, Catherine Lawrence, Jules Manser, Martin T. O'Reilly, James Robinson, Helen Sherwood-Taylor, Serena Tierney, Catalina A. Vallejos, Sebastian J. Vollmer, Kirstie J. Whitaker
Design choices for productive, secure, data-intensive research at scale in the cloud. CoRR (2019)
Sebastian J. Vollmer, Bilal A. Mateen, Gergo Bohner, Franz J. Király, Rayid Ghani, Pall Jonsson, Sarah Cumbers, Adrian Jonas, Katherine S. L. McAllister, Puja Myles, David Granger, Mark Birse, Richard Branson, Karel G. M. Moons, Gary S. Collins, John P. A. Ioannidis, Chris C. Holmes, Harry Hemingway
Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness. CoRR (2018)
Louis J. M. Aslett, Tigran Nagapetyan, Sebastian J. Vollmer
Multilevel Monte Carlo for Reliability Theory. Reliab. Eng. Syst. Saf. 165 (2017)
Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer
Relativistic Monte Carlo. AISTATS (2017)
Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian J. Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server. J. Mach. Learn. Res. 18 (2017)
Yee Whye Teh, Alexandre H. Thiery, Sebastian J. Vollmer
Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics. J. Mach. Learn. Res. 17 (2016)
Sebastian J. Vollmer, Konstantinos C. Zygalakis, Yee Whye Teh
Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics. J. Mach. Learn. Res. 17 (2016)
Jack Gorham, Andrew B. Duncan, Sebastian J. Vollmer, Lester W. Mackey
Measuring Sample Quality with Diffusions. CoRR (2016)
Yee Whye Teh, Leonard Hasenclever, Thibaut Lienart, Sebastian J. Vollmer, Stefan Webb, Balaji Lakshminarayanan, Charles Blundell
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server. CoRR (2015)
Sebastian J. Vollmer
Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors. SIAM/ASA J. Uncertain. Quantification 3 (1) (2015)