| 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 | 769 | 2019 |
| Towards Causal Representation Learning B Schölkopf*, F Locatello*, S Bauer, NR Ke, N Kalchbrenner, A Goyal, ... Proceedings of the IEEE, 2021 | 232 | 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 | 185 | 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 | 126 | 2019 |
| 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 | 120 | 2020 |
| 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 | 104 | 2019 |
| 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 | 97 | 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 | 93 | 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 | 64 | 2019 |
| 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 | 51 | 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 | 32 | 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 | 30 | 2019 |
| 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 | 29 | 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 | 28 | 2017 |
| 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 | 27 | 2021 |
| 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 | 27 | 2021 |
| Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees F Locatello, M Tschannen, G Rätsch, M Jaggi NIPS 2017 - Advances in Neural Information Processing Systems, 2017 | 25 | 2017 |
| Boosting Black Box Variational Inference F Locatello, G Dresdner, R Khanna, I Valera, G Rätsch NeurIPS 2018 - Advances in Neural Information Processing Systems (Spotlight), 2018 | 22 | 2018 |
| Competitive Training of Mixtures of Independent Deep Generative Models F Locatello, D Vincent, I Tolstikhin, G Rätsch, S Gelly, B Schölkopf arXiv preprint arXiv:1804.11130, 2018 | 22* | 2018 |
| 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 | 21 | 2020 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
