| 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 | 354 | 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 | 53 | 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 | 52 | 2018 |
| 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 | 51 | 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 | 42 | 2017 |
| 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 | 35 | 2020 |
| 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 | 21 | 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 | 21 | 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 | 21 | 2019 |
| 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 | 20 | 2017 |
| 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 | 19 | 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 | 17 | 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 | 16* | 2018 |
| On Matching Pursuit and Coordinate Descent F Locatello, A Raj, SP Reddy, G Rätsch, B Schölkopf, SU Stich, M Jaggi ICML 2018 - Proceedings of the 35th International Conference on Machine Learning, 2018 | 16* | 2018 |
| 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 | 14 | 2018 |
| 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 | 12 | 2019 |
| Stochastic Frank-Wolfe for Composite Convex Minimization F Locatello, A Yurtsever, O Fercoq, V Cevher NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, 2019 | 7* | 2019 |
| Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization G Négiar, G Dresdner, A Tsai, LE Ghaoui, F Locatello, RM Freund, ... ICML 2020 - Proceedings of the 37th International Conference on Machine Learning, 2020 | 5 | 2020 |
| Is independence all you need? on the generalization of representations learned from correlated data F Träuble, E Creager, N Kilbertus, A Goyal, F Locatello, B Schölkopf, ... arXiv preprint arXiv:2006.07886, 2020 | 4 | 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 | 2 | 2020 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
