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ORCIDSebastian J. Vollmer
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2020 – today
- 2025
[c9]Yilong Zang, Lingfei Ren, Yue Li, Zhikang Wang, David Antony Selby, Zheng Wang, Sebastian Josef Vollmer, Hongzhi Yin, Jiangning Song, Junhang Wu:
Rethinking Cancer Gene Identification Through Graph Anomaly Analysis. AAAI 2025: 13161-13169- [i25]Yousef Koka, David Antony Selby, Gerrit Großmann, Sebastian J. Vollmer:
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning. CoRR abs/2502.03946 (2025) - [i24]Sumantrak Mukherjee, Mouad Elhamdi, George Mohler, David Antony Selby, Yao Xie, Sebastian J. Vollmer, Gerrit Grossmann:
Neural Spatiotemporal Point Processes: Trends and Challenges. CoRR abs/2502.09341 (2025) - [i23]Saif Ur Rehman Khan
, Muhammad Nabeel Asim, Sebastian J. Vollmer, Andreas Dengel:
AI-Driven Diabetic Retinopathy Diagnosis Enhancement through Image Processing and Salp Swarm Algorithm-Optimized Ensemble Network. CoRR abs/2503.14209 (2025) - [i22]Yahya Aalaila, Gerrit Großmann, Sumantrak Mukherjee, Jonas Wahl, Sebastian J. Vollmer:
When Counterfactual Reasoning Fails: Chaos and Real-World Complexity. CoRR abs/2503.23820 (2025) - 2024
- [j14]Ahtisham Fazeel Abbasi, Muhammad Nabeel Asim, Sheraz Ahmed, Sebastian J. Vollmer, Andreas Dengel:
Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases. Frontiers Artif. Intell. 7 (2024) - [j13]James Liley, Gergo Bohner, Samuel R. Emerson, Bilal A. Mateen, Katie Borland, David Carr, Scott Heald, Samuel D. Oduro, Jill Ireland, Keith Moffat, Rachel Porteous, Stephen Riddell, Simon Rogers, Ioanna Thoma, Nathan Cunningham, Chris Holmes, Katrina Payne, Sebastian J. Vollmer, Catalina A. Vallejos, Louis J. M. Aslett:
Publisher Correction: Development and assessment of a machine learning tool for predicting emergency admission in Scotland. npj Digit. Medicine 7(1) (2024) - [j12]James Liley, Gergo Bohner, Samuel R. Emerson, Bilal A. Mateen, Katie Borland, David Carr, Scott Heald, Samuel D. Oduro, Jill Ireland, Keith Moffat, Rachel Porteous, Stephen Riddell, Simon Rogers, Ioanna Thoma, Nathan Cunningham, Chris Holmes, Katrina Payne, Sebastian J. Vollmer, Catalina A. Vallejos, Louis J. M. Aslett:
Development and assessment of a machine learning tool for predicting emergency admission in Scotland. npj Digit. Medicine 7(1) (2024) - [c8]Alexander Günther, Sebastian J. Vollmer, Peter Liggesmeyer:
Confidence-Aware Fault Trees. ARES 2024: 39:1-39:7 - [c7]Gerrit Großmann, Sumantrak Mukherjee, Sebastian J. Vollmer:
Peculiarities of Counterfactual Point Process Generation. STCausal@SIGSPATIAL 2024: 11-22 - [i21]Rahul Sharma, Sergey Redyuk, Sumantrak Mukherjee, Andrea Sipka, Sebastian J. Vollmer, David Antony Selby:
X Hacking: The Threat of Misguided AutoML. CoRR abs/2401.08513 (2024) - [i20]David Antony Selby, Kai Spriestersbach, Yuichiro Iwashita, Dennis Bappert, Archana Warrier, Sumantrak Mukherjee, Muhammad Nabeel Asim, Koichi Kise, Sebastian J. Vollmer:
Quantitative knowledge retrieval from large language models. CoRR abs/2402.07770 (2024) - [i19]Daniel Reti, Norman Becker, Tillmann Angeli, Anasuya Chattopadhyay, Daniel Schneider, Sebastian J. Vollmer, Hans D. Schotten:
Act as a Honeytoken Generator! An Investigation into Honeytoken Generation with Large Language Models. CoRR abs/2404.16118 (2024) - [i18]Gerrit Großmann, Sebastian J. Vollmer:
icon: Fast Simulation of Epidemics on Coevolving Networks. CoRR abs/2410.04987 (2024) - [i17]Sumantrak Mukherjee, Mengyan Zhang, Seth R. Flaxman, Sebastian J. Vollmer:
Graph Agnostic Causal Bayesian Optimisation. CoRR abs/2411.03028 (2024) - [i16]Yilong Zang, Lingfei Ren, Yue Li, Zhikang Wang, David Antony Selby, Zheng Wang, Sebastian J. Vollmer, Hongzhi Yin, Jiangning Song, Junhang Wu:
Rethinking Cancer Gene Identification through Graph Anomaly Analysis. CoRR abs/2412.17240 (2024) - 2023
- [j11]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): 110 (2023) - [j10]Florian Pfisterer, Siyi Wei, Sebastian J. Vollmer, Michel Lang, Bernd Bischl:
Fairness Audits and Debiasing Using \pkg{mlr3fairness}. R J. 15(1): 234-253 (2023) - [c6]Jen Ning Lim, Sebastian J. Vollmer, Lorenz Wolf, Andrew B. Duncan:
Energy-Based Models for Functional Data using Path Measure Tilting. AISTATS 2023: 1904-1923 - [c5]Tobias Schröder, Zijing Ou, Jen Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew B. Duncan:
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. NeurIPS 2023 - [i15]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 abs/2307.06431 (2023) - 2022
- [j9]Raphael Sonabend, Andreas Bender, Sebastian J. Vollmer:
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures. Bioinform. 38(17): 4178-4184 (2022) - [c4]Sahra Ghalebikesabi, Harry Wilde, Jack Jewson, Arnaud Doucet, Sebastian J. Vollmer, Chris C. Holmes:
Mitigating statistical bias within differentially private synthetic data. UAI 2022: 696-705 - [i14]Jen Ning Lim, Sebastian J. Vollmer, Lorenz Wolf, Andrew B. Duncan:
F-EBM: Energy Based Learning of Functional Data. CoRR abs/2202.01929 (2022) - [i13]Oscar Giles, Kasra Hosseini, Grigorios Mingas, Oliver Strickson, Louise Bowler, Camila Rangel Smith, Harrison Wilde, Jen Ning Lim, Bilal A. Mateen, Kasun Amarasinghe, Rayid Ghani, Alison J. Heppenstall, Nik Lomax, Nick Malleson, Martin T. O'Reilly, Sebastian J. Vollmer:
Faking feature importance: A cautionary tale on the use of differentially-private synthetic data. CoRR abs/2203.01363 (2022) - [i12]Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sebastian J. Vollmer:
Flexible Group Fairness Metrics for Survival Analysis. CoRR abs/2206.03256 (2022) - 2021
- [c3]Harrison Wilde, Jack Jewson, Sebastian J. Vollmer, Chris C. Holmes:
Foundations of Bayesian Learning from Synthetic Data. AISTATS 2021: 541-549 - [c2]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: 3916-3924 - [i11]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 abs/2108.10934 (2021) - [i10]Raphael Sonabend, Andreas Bender, Sebastian J. Vollmer:
Evaluation of survival distribution predictions with discrimination measures. CoRR abs/2112.04828 (2021) - 2020
- [j8]Anthony D. Blaom, Franz J. Király, Thibaut Liénart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer:
MLJ: A Julia package for composable machine learning. J. Open Source Softw. 5(55): 2704 (2020) - [j7]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): 554-556 (2020) - [j6]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): 507-524 (2020) - [i9]Anthony D. Blaom, Franz J. Király, Thibaut Liénart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer:
MLJ: A Julia package for composable Machine Learning. CoRR abs/2007.12285 (2020) - [i8]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 abs/2010.11530 (2020) - [i7]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 abs/2011.02407 (2020) - [i6]Harrison Wilde, Jack Jewson, Sebastian J. Vollmer, Chris C. Holmes:
Foundations of Bayesian Learning from Synthetic Data. CoRR abs/2011.08299 (2020) - [i5]Anthony D. Blaom, Sebastian J. Vollmer:
Flexible model composition in machine learning and its implementation in MLJ. CoRR abs/2012.15505 (2020)
2010 – 2019
- 2019
- [i4]Diego Arenas, Jon Atkins, Clare Austin, David Beavan, Alvaro Cabrejas 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 abs/1908.08737 (2019) - 2018
- [i3]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 abs/1812.10404 (2018) - 2017
- [j5]Leonard Hasenclever, Stefan Webb, Thibaut Liénart, 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: 106:1-106:37 (2017) - [j4]Louis J. M. Aslett, Tigran Nagapetyan, Sebastian J. Vollmer:
Multilevel Monte Carlo for Reliability Theory. Reliab. Eng. Syst. Saf. 165: 188-196 (2017) - [c1]Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer:
Relativistic Monte Carlo. AISTATS 2017: 1236-1245 - 2016
- [j3]Yee Whye Teh, Alexandre H. Thiéry, Sebastian J. Vollmer:
Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics. J. Mach. Learn. Res. 17: 7:1-7:33 (2016) - [j2]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: 159:1-159:48 (2016) - [i2]Jack Gorham, Andrew B. Duncan, Sebastian J. Vollmer, Lester W. Mackey:
Measuring Sample Quality with Diffusions. CoRR abs/1611.06972 (2016) - 2015
- [j1]Sebastian J. Vollmer:
Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors. SIAM/ASA J. Uncertain. Quantification 3(1): 535-561 (2015) - [i1]Yee Whye Teh, Leonard Hasenclever, Thibaut Liénart, Sebastian J. Vollmer, Stefan Webb, Balaji Lakshminarayanan, Charles Blundell:
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server. CoRR abs/1512.09327 (2015)
Coauthor Index
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last updated on 2025-05-03 00:05 CEST by the dblp team
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