| 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 | 757 | 2019 |
| 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 | 205 | 2021 |
| 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 | 124 | 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, 781, 2018 | 95 | 2018 |
| 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 | 91 | 2019 |
| 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 | 88* | 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 | 74 | 2019 |
| 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 | 63 | 2019 |
| 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 | 50 | 2020 |
| 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 | 45 | 2020 |
| 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 | 32 | 2020 |
| 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 | 31 | 2020 |
| Scalable variational inference for dynamical systems NS Gorbach, S Bauer, JM Buhmann Neural Information Processing Systems (NeurIPS) 2017, 2017 | 31 | 2017 |
| 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 | 29 | 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 | 28* | 2019 |
| Towards causal representation learning B Schölkopf, F Locatello, S Bauer, NR Ke, N Kalchbrenner, A Goyal, ... arXiv preprint arXiv:2102.11107, 2021 | 27 | 2021 |
| 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 | 26 | 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 | 26 | 2018 |
| 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 | 25 | 2020 |
| Multidimensional contrast limited adaptive histogram equalization V Stimper, S Bauer, R Ernstorfer, B Schölkopf, RP Xian IEEE Access 7, 165437-165447, 2019 | 25 | 2019 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
