| 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 | 1307 | 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 | 845 | 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 | 203 | 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 | 165* | 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 | 163 | 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 | 147 | 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 | 139 | 2018 |
| 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 | 114 | 2019 |
| Machine learning-enabled high-entropy alloy discovery Z Rao, PY Tung, R Xie, Y Wei, H Zhang, A Ferrari, TPC Klaver, F Körmann, ... Science, 2022 | 112 | 2022 |
| 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 | 108 | 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 | 99 | 2020 |
| 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 | 94 | 2020 |
| 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 | 91 | 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 | 67 | 2020 |
| Bayesian Structure Learning with Generative Flow Networks T Deleu, A Góis, C Emezue, M Rankawat, S Lacoste-Julien, S Bauer, ... Conference on Uncertainty in Artificial Intelligence (UAI), 2022 | 65 | 2022 |
| 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), 1058, 2021 | 54 | 2021 |
| 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 | 52 | 2020 |
| Multidimensional contrast limited adaptive histogram equalization V Stimper, S Bauer, R Ernstorfer, B Schölkopf, RP Xian IEEE Access 7, 165437-165447, 2019 | 51 | 2019 |
| Trifinger: An open-source robot for learning dexterity M Wüthrich, F Widmaier, F Grimminger, J Akpo, S Joshi, V Agrawal, ... Conference on Robot Learning (CoRL) 2020, 2020 | 50 | 2020 |
| Diffusion models for video prediction and infilling T Höppe, A Mehrjou, S Bauer, D Nielsen, A Dittadi Transactions on Machine Learning Research (TMLR), 2022 | 49 | 2022 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHEmail verificata su tuebingen.mpg.de
