Gresele L, Rubenstein PK, Mehrjou A, Locatello F, Schölkopf B. 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. ML Research Press; 2019:217-227.
LibreCat: https://research-explorer.ista.ac.at/record/14189
arXiv: 1905.06642

Locatello F, Abbati G, Rainforth T, Bauer S, Schölkopf B, Bachem O. On the fairness of disentangled representations. In: Advances in Neural Information Processing Systems. Vol 32. ; 2019:14611–14624.
LibreCat: https://research-explorer.ista.ac.at/record/14197
arXiv: 1905.13662

Locatello F, Yurtsever A, Fercoq O, Cevher V. Stochastic Frank-Wolfe for composite convex minimization. In: Advances in Neural Information Processing Systems. Vol 32. ; 2019:14291–14301.
LibreCat: https://research-explorer.ista.ac.at/record/14191
arXiv: 1901.10348

Steenkiste S van, Locatello F, Schmidhuber J, Bachem O. Are disentangled representations helpful for abstract visual reasoning? In: Advances in Neural Information Processing Systems. Vol 32. ; 2019.
LibreCat: https://research-explorer.ista.ac.at/record/14193
arXiv: 1905.12506

Locatello F, Bauer S, Lucic M, et al. Challenging common assumptions in the unsupervised learning of disentangled representations. In: Proceedings of the 36th International Conference on Machine Learning. Vol 97. ML Research Press; 2019:4114-4124.
LibreCat: https://research-explorer.ista.ac.at/record/14200
arXiv: 1811.12359