Stefan Bauer
Stefan Bauer
MPI for Intelligent Systems
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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
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
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
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
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
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
Scalable variational inference for dynamical systems
NS Gorbach, S Bauer, JM Buhmann
Neural Information Processing Systems (NeurIPS) 2017, 2017
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
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
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
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
The arrow of time in multivariate time series
S Bauer, B Schölkopf, J Peters
International Conference on Machine Learning (ICML) 2016, 2016
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
Adaptive skip intervals: Temporal abstraction for recurrent dynamical models
A Neitz, G Parascandolo, S Bauer, B Schölkopf
Neural Information Processing Systems (NeurIPS) 2018, 2018
Model selection for gaussian process regression
NS Gorbach, AA Bian, B Fischer, S Bauer, JM Buhmann
German Conference on Pattern Recognition, 306-318, 2017
Disentangled state space representations
Đ Miladinović, MW Gondal, B Schölkopf, JM Buhmann, S Bauer
Deep Generative Models for Highly Structured Data Workshop - International …, 2019
SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species
Đ Miladinović, C Muheim, S Bauer, A Spinnler, D Noain, M Bandarabadi, ...
PLoS computational biology 15 (4), e1006968, 2019
Multi-organ cancer classification and survival analysis
S Bauer, N Carion, P Schüffler, T Fuchs, P Wild, JM Buhmann
Machine Learning for Health Workshop – Neural Information Processing Systems …, 2016
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
Odin: Ode-informed regression for parameter and state inference in time-continuous dynamical systems
P Wenk, G Abbati, MA Osborne, B Schölkopf, A Krause, S Bauer
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6364-6371, 2020
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