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Stefan Bauer
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Challenging common assumptions in the unsupervised learning of disentangled representations
F Locatello, S Bauer, M Lucic, G Raetsch, S Gelly, B Schölkopf, O Bachem
International Conference on Machine Learning (ICML) 2019, 2019
8562019
Toward causal representation learning
B Schölkopf, F Locatello, S Bauer, NR Ke, N Kalchbrenner, A Goyal, ...
Proceedings of the IEEE 109 (5), 612-634, 2021
3232021
On the fairness of disentangled representations
F Locatello, G Abbati, T Rainforth, S Bauer, B Schölkopf, O Bachem
Neural Information Processing Systems (NeurIPS) 2019, 2019
1392019
Disentangling factors of variation using few labels
F Locatello, M Tschannen, S Bauer, G Rätsch, B Schölkopf, O Bachem
International Conference on Learning Representations (ICLR) 2020, 2019
1032019
Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness
R Suter, D Miladinovic, B Schölkopf, S Bauer
International Conference on Machine Learning (ICML) 2019, 2019
100*2019
Automatic human sleep stage scoring using deep neural networks
A Malafeev, D Laptev, S Bauer, X Omlin, A Wierzbicka, A Wichniak, ...
Frontiers in neuroscience 12, 781, 2018
1002018
Learning neural causal models from unknown interventions
NR Ke, O Bilaniuk, A Goyal, S Bauer, H Larochelle, B Schölkopf, ...
arXiv preprint arXiv:1910.01075, 2019
892019
On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
MW Gondal, M Wüthrich, Đ Miladinović, F Locatello, M Breidt, V Volchkov, ...
Neural Information Processing Systems (NeurIPS) 2019, 2019
712019
Causalworld: A robotic manipulation benchmark for causal structure and transfer learning
O Ahmed, F Träuble, A Goyal, A Neitz, M Wüthrich, Y Bengio, B Schölkopf, ...
International Conference on Learning Representations (ICLR) 2021, 2020
602020
Clinical predictive models for COVID-19: systematic study
P Schwab, ADM Schütte, B Dietz, S Bauer
Journal of medical Internet research 22 (10), e21439, 2020
542020
Learning counterfactual representations for estimating individual dose-response curves
P Schwab, L Linhardt, S Bauer, JM Buhmann, W Karlen
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5612-5619, 2020
422020
On Disentangled Representations Learned From Correlated Data
F Träuble, E Creager, N Kilbertus, F Locatello, A Dittadi, A Goyal, ...
International Conference on Machine Learning (ICML), 2020
392020
On the Transfer of Disentangled Representations in Realistic Settings
A Dittadi, F Träuble, F Locatello, M Wüthrich, V Agrawal, O Winther, ...
International Conference on Learning Representations (ICLR) 2021, 2020
382020
Scalable variational inference for dynamical systems
NS Gorbach, S Bauer, JM Buhmann
Neural Information Processing Systems (NeurIPS) 2017, 2017
332017
Learning stable and predictive structures in kinetic systems
N Pfister, S Bauer, J Peters
Proceedings of the National Academy of Sciences 116 (51), 25405-25411, 2019
32*2019
Real-time prediction of COVID-19 related mortality using electronic health records
P Schwab, A Mehrjou, S Parbhoo, LA Celi, J Hetzel, M Hofer, B Schölkopf, ...
Nature communications 12 (1), 1-16, 2021
312021
Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs
P Wenk, A Gotovos, S Bauer, NS Gorbach, A Krause, JM Buhmann
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
302019
Adaptive skip intervals: Temporal abstraction for recurrent dynamical models
A Neitz, G Parascandolo, S Bauer, B Schölkopf
Neural Information Processing Systems (NeurIPS) 2018, 2018
292018
A sober look at the unsupervised learning of disentangled representations and their evaluation
F Locatello, S Bauer, M Lucic, G Rätsch, S Gelly, B Schölkopf, O Bachem
Journal of Machine Learning Research (JMLR), 2020
282020
Multidimensional contrast limited adaptive histogram equalization
V Stimper, S Bauer, R Ernstorfer, B Schölkopf, RP Xian
IEEE Access 7, 165437-165447, 2019
282019
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Articles 1–20