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
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
2017 | Conference Paper | IST-REx-ID: 14205 |
F. Locatello, R. Khanna, M. Tschannen, and M. Jaggi, “A unified optimization view on generalized matching pursuit and Frank-Wolfe,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States, 2017, vol. 54, pp. 860–868.
[Preprint]
View
| Download Preprint (ext.)
| arXiv