| Challenging common assumptions in the unsupervised learning of disentangled representations F Locatello, S Bauer, M Lucic, G Rätsch, S Gelly, B Schölkopf, O Bachem ICML 2019 - Proceedings of the 36th International Conference on Machine …, 2019 | 1228 | 2019 |
| Towards Causal Representation Learning B Schölkopf*, F Locatello*, S Bauer, NR Ke, N Kalchbrenner, A Goyal, ... Proceedings of the IEEE, 2021 | 754 | 2021 |
| Object-Centric Learning with Slot Attention F Locatello*, D Weissenborn, T Unterthiner, A Mahendran, G Heigold, ... NeurIPS 2020 - Thirty-fourth Conference on Neural Information Processing …, 2020 | 476 | 2020 |
| Weakly-Supervised Disentanglement Without Compromises F Locatello, B Poole, G Rätsch, B Schölkopf, O Bachem, M Tschannen ICML 2020 - Proceedings of the 37th International Conference on Machine Learning, 2020 | 235 | 2020 |
| On the Fairness of Disentangled Representations F Locatello, G Abbati, T Rainforth, S Bauer, B Schölkopf, O Bachem NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, 2019 | 192 | 2019 |
| Are Disentangled Representations Helpful for Abstract Visual Reasoning? S van Steenkiste, F Locatello, J Schmidhuber, O Bachem NeurIPS 2019: Thirty-third Conference on Neural Information Processing Systems, 2019 | 173 | 2019 |
| Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style J von Kügelgen, Y Sharma, L Gresele, W Brendel, B Schölkopf, ... NeurIPS 2021 - Thirty-fifth Conference on Neural Information Processing Systems, 2021 | 161 | 2021 |
| SOM-VAE: Interpretable discrete representation learning on time series V Fortuin, M Hüser, F Locatello, H Strathmann, G Rätsch ICLR 2019 - Seventh International Conference on Learning Representations, 2018 | 151 | 2018 |
| Disentangling factors of variation using few labels F Locatello, M Tschannen, S Bauer, G Rätsch, B Schölkopf, O Bachem ICLR 2020 - 8th International Conference on Learning Representations, 2020 | 150 | 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, ... NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, 2019 | 105 | 2019 |
| On Disentangled Representations Learned From Correlated Data F Träuble, E Creager, N Kilbertus, F Locatello, A Dittadi, A Goyal, ... ICML 2021 - Proceedings of the 38th International Conference on Machine Learning, 2021 | 85 | 2021 |
| A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe F Locatello, R Khanna, M Tschannen, M Jaggi AISTATS 2017 - Proceedings of the 20th International Conference on Artifcial …, 2017 | 62 | 2017 |
| On the transfer of disentangled representations in realistic settings A Dittadi, F Träuble, F Locatello, M Wüthrich, V Agrawal, O Winther, ... ICLR 2021 - 9th International Conference on Learning Representations, 2020 | 60 | 2020 |
| The incomplete rosetta stone problem: Identifiability results for multi-view nonlinear ica L Gresele, PK Rubenstein, A Mehrjou, F Locatello, B Schölkopf UAI 2019 - Conference on Uncertainty in Artificial Intelligence, 2019 | 58 | 2019 |
| 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 | 47 | 2020 |
| SCIM: universal single-cell matching with unpaired feature sets SG Stark, J Ficek, F Locatello, X Bonilla, S Chevrier, F Singer, G Rätsch, ... Bioinformatics 36 (Supplement_2), i919-i927, 2020 | 40 | 2020 |
| Visual representation learning does not generalize strongly within the same domain L Schott, J von Kügelgen, F Träuble, P Gehler, C Russell, M Bethge, ... ICLR 2022, 2022 | 38 | 2022 |
| A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming A Yurtsever, O Fercoq, F Locatello, V Cevher ICML 2018 - Proceedings of the 35th International Conference on Machine Learning, 2018 | 38 | 2018 |
| Boosting Variational Inference: an Optimization Perspective F Locatello, R Khanna, J Ghosh, G Rätsch AISTATS 2018 - Proceedings of the 21th International Conference on Artifcial …, 2017 | 37 | 2017 |
| Assaying out-of-distribution generalization in transfer learning F Wenzel, A Dittadi, PV Gehler, CJ Simon-Gabriel, M Horn, D Zietlow, ... NeurIPS 2022, 2022 | 30 | 2022 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHEmail verificata su tuebingen.mpg.de
