| 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 | 1619 | 2019 |
| Towards Causal Representation Learning B Schölkopf*, F Locatello*, S Bauer, NR Ke, N Kalchbrenner, A Goyal, ... Proceedings of the IEEE, 2021 | 1317 | 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 | 819 | 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 | 353 | 2020 |
| 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 | 303 | 2021 |
| 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 | 250 | 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 | 217 | 2019 |
| 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 | 201 | 2020 |
| 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 | 191 | 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, ... NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, 2019 | 148 | 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 | 134 | 2021 |
| Bridging the gap to real-world object-centric learning M Seitzer, M Horn, A Zadaianchuk, D Zietlow, T Xiao, CJ Simon-Gabriel, ... ICLR 2023, 2023 | 115 | 2023 |
| 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 | 92 | 2019 |
| 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 | 90 | 2020 |
| Relative representations enable zero-shot latent space communication L Moschella, V Maiorca, M Fumero, A Norelli, F Locatello, E Rodolà ICLR 2023 (Oral), 2023 | 80 | 2023 |
| Score matching enables causal discovery of nonlinear additive noise models P Rolland, V Cevher, M Kleindessner, C Russel, B Schölkopf, D Janzing, ... ICML 2022, 2022 | 78 | 2022 |
| Assaying out-of-distribution generalization in transfer learning F Wenzel, A Dittadi, PV Gehler, CJ Simon-Gabriel, M Horn, D Zietlow, ... NeurIPS 2022, 2022 | 69 | 2022 |
| 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 | 68 | 2020 |
| Generalization and robustness implications in object-centric learning A Dittadi, S Papa, M De Vita, B Schölkopf, O Winther, F Locatello ICML 2022, 2022 | 67 | 2022 |
| 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 | 66 | 2022 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHEmail verificata su tuebingen.mpg.de