Please note that ISTA Research Explorer no longer supports Internet Explorer versions 8 or 9 (or earlier).
We recommend upgrading to the latest Internet Explorer, Google Chrome, or Firefox.
71 Publications
2020 | Conference Paper | IST-REx-ID: 14186 | 
F. Locatello et al., “A commentary on the unsupervised learning of disentangled representations,” in The 34th AAAI Conference on Artificial Intelligence, New York, NY, United States, 2020, vol. 34, no. 9, pp. 13681–13684.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 14188 |
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, and M. Tschannen, “Weakly-supervised disentanglement without compromises,” in Proceedings of the 37th International Conference on Machine Learning, Virtual, 2020, vol. 119, pp. 6348–6359.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 14187 |
G. Négiar et al., “Stochastic Frank-Wolfe for constrained finite-sum minimization,” in Proceedings of the 37th International Conference on Machine Learning, Virtual, 2020, vol. 119, pp. 7253–7262.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2020 | Journal Article | IST-REx-ID: 14195 |
F. Locatello et al., “A sober look at the unsupervised learning of disentangled representations and their evaluation,” Journal of Machine Learning Research, vol. 21. MIT Press, 2020.
[Published Version]
View
| Download Published Version (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 14326 |
F. Locatello et al., “Object-centric learning with slot attention,” in Advances in Neural Information Processing Systems, Virtual, 2020, vol. 33, pp. 11525–11538.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 14184 |
F. Locatello, M. Tschannen, S. Bauer, G. Rätsch, B. Schölkopf, and O. Bachem, “Disentangling factors of variation using few labels,” in 8th International Conference on Learning Representations, Virtual, 2019.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 14189 |
L. Gresele, P. K. Rubenstein, A. Mehrjou, F. Locatello, and B. Schölkopf, “The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA,” in Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence, Tel Aviv, Israel, 2019, vol. 115, pp. 217–227.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 14197 |
F. Locatello, G. Abbati, T. Rainforth, S. Bauer, B. Schölkopf, and O. Bachem, “On the fairness of disentangled representations,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 14611–14624.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 14191 |
F. Locatello, A. Yurtsever, O. Fercoq, and V. Cevher, “Stochastic Frank-Wolfe for composite convex minimization,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 14291–14301.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 14193 |
S. van Steenkiste, F. Locatello, J. Schmidhuber, and O. Bachem, “Are disentangled representations helpful for abstract visual reasoning?,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 14200 |
F. Locatello et al., “Challenging common assumptions in the unsupervised learning of disentangled representations,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, United States, 2019, vol. 97, pp. 4114–4124.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 14190 |
M. W. Gondal et al., “On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 14202 |
F. Locatello, G. Dresdner, R. Khanna, I. Valera, and G. Rätsch, “Boosting black box variational inference,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2018, vol. 31.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 14201 |
F. Locatello, R. Khanna, J. Ghosh, and G. Rätsch, “Boosting variational inference: An optimization perspective,” in Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, Playa Blanca, Lanzarote, 2018, vol. 84, pp. 464–472.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 14198 |
V. Fortuin, M. Hüser, F. Locatello, H. Strathmann, and G. Rätsch, “SOM-VAE: Interpretable discrete representation learning on time series,” in International Conference on Learning Representations, New Orleans, LA, United States, 2018.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 14203 |
A. Yurtsever, O. Fercoq, F. Locatello, and V. Cevher, “A conditional gradient framework for composite convex minimization with applications to semidefinite programming,” in Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 5727–5736.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 14204 |
F. Locatello et al., “On matching pursuit and coordinate descent,” in Proceedings of the 35th International Conference on Machine Learning, 2018, vol. 80, pp. 3198–3207.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 14224 |
F. Locatello, D. Vincent, I. Tolstikhin, G. Ratsch, S. Gelly, and B. Scholkopf, “Clustering meets implicit generative models,” in 6th International Conference on Learning Representations, Vancouver, Canada, 2018.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2018 | Preprint | IST-REx-ID: 14327 |
F. Locatello, D. Vincent, I. Tolstikhin, G. Rätsch, S. Gelly, and B. Schölkopf, “Competitive training of mixtures of independent deep generative models,” arXiv. .
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2017 | Conference Paper | IST-REx-ID: 14206 |
F. Locatello, M. Tschannen, G. Rätsch, and M. Jaggi, “Greedy algorithms for cone constrained optimization with convergence guarantees,” in Advances in Neural Information Processing Systems, Long Beach, CA, United States, 2017.
[Preprint]
View
| Download Preprint (ext.)
| arXiv