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9078 Publications
2019 | Conference Paper | IST-REx-ID: 14189 | 
Gresele, L., Rubenstein, P. K., Mehrjou, A., Locatello, F., & Schölkopf, B. (2019). The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA. In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (Vol. 115, pp. 217–227). Tel Aviv, Israel: ML Research Press.
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| arXiv
2019 | Conference Paper | IST-REx-ID: 14197 |
Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., & Bachem, O. (2019). On the fairness of disentangled representations. In Advances in Neural Information Processing Systems (Vol. 32, pp. 14611–14624). Vancouver, Canada.
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| arXiv
2019 | Conference Paper | IST-REx-ID: 14191 |
Locatello, F., Yurtsever, A., Fercoq, O., & Cevher, V. (2019). Stochastic Frank-Wolfe for composite convex minimization. In Advances in Neural Information Processing Systems (Vol. 32, pp. 14291–14301). Vancouver, Canada.
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| arXiv
2019 | Conference Paper | IST-REx-ID: 14193 |
Steenkiste, S. van, Locatello, F., Schmidhuber, J., & Bachem, O. (2019). Are disentangled representations helpful for abstract visual reasoning? In Advances in Neural Information Processing Systems (Vol. 32). Vancouver, Canada.
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| arXiv
2019 | Conference Paper | IST-REx-ID: 14200 |
Locatello, F., Bauer, S., Lucic, M., Rätsch, G., Gelly, S., Schölkopf, B., & Bachem, O. (2019). Challenging common assumptions in the unsupervised learning of disentangled representations. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 4114–4124). Long Beach, CA, United States: ML Research Press.
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| arXiv