| 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 | 402 | 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 | 64 | 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 | 60 | 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 | 49* | 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 | 39 | 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 | 27 | 2019 |
| Scalable variational inference for dynamical systems NS Gorbach, S Bauer, JM Buhmann Neural Information Processing Systems (NeurIPS) 2017, 2017 | 25 | 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 | 23* | 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 | 23 | 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 | 17* | 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 | 17 | 2019 |
| The arrow of time in multivariate time series S Bauer, B Schölkopf, J Peters International Conference on Machine Learning (ICML) 2016, 2016 | 17 | 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 | 16 | 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 | 16 | 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 | 14* | 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 | 13 | 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 | 13 | 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 | 13 | 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 | 12 | 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 | 12 | 2020 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
